Merge pull request #2 from sasan-j/feature/remove-nexus

apis.py

@@ -1,54 +0,0 @@
-from geopy.geocoders import Nominatim
-
-
-###################################################
-# Functions we want to articulate (APIs calls): ###
-###################################################
-
-########################################################################################
-# Functions called  in the articulated functions (not directly called by the model): ###
-########################################################################################
-
-geolocator = Nominatim(user_agent="MyApp")
-
-def find_precise_place(lat, lon):
-    location = geolocator.reverse(str(lat) +", " + str(lon))
-    return location.raw.get('display_name', {})
-
-def find_coordinates(address):
-    coord = geolocator.geocode(address)
-    lat = coord.latitude
-    lon = coord.longitude
-    return(lat,lon)
-
-
-def check_city_coordinates(lat = "", lon = "", city = "", **kwargs):
-    """
-    :param lat: latitude
-    :param lon: longitude
-    :param city: name of the city
-
-    Checks if the coordinates correspond to the city, if not update the coordinate to correspond to the city
-    """
-    if lat != "0" and lon != "0":
-        reverse = partial(geolocator.reverse, language="en")
-        location = reverse(f"{lat}, {lon}")
-        address = location.raw.get('address', {})
-        city = address.get('city') or address.get('town') or address.get('village') or address.get('county')
-    else : 
-        reverse = partial(geolocator.reverse, language="en")
-        location = reverse(f"{lat}, {lon}")
-        address = location.raw.get('address', {})
-        city_name = address.get('city') or address.get('town') or address.get('village') or address.get('county')
-        if city_name is None :
-            city_name = 'not_found'
-        print(city_name)
-        if city_name.lower() != city.lower():
-            coord = geolocator.geocode(city )
-            if coord is None:
-                coord = geolocator.geocode(city)
-            lat = coord.latitude
-            lon = coord.longitude
-    return lat, lon, city
-
-

kitt/core/stt.py

@@ -0,0 +1,39 @@
+import copy
+import time
+
+import gradio as gr
+import numpy as np
+import torch
+import torchaudio
+from loguru import logger
+from transformers import pipeline
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+transcriber = pipeline(
+    "automatic-speech-recognition", model="openai/whisper-base.en", device=device
+)
+
+
+def save_audio_as_wav(data, sample_rate, file_path):
+    # make a tensor from the numpy array
+    data = torch.tensor(data).reshape(1, -1)
+    torchaudio.save(
+        file_path, data, sample_rate=sample_rate, bits_per_sample=16, encoding="PCM_S"
+    )
+
+
+def save_and_transcribe_audio(audio):
+    sample_rate, data = audio
+    try:
+        # add timestamp to file name
+        filename = f"recordings/audio{time.time()}.wav"
+        save_audio_as_wav(data, sample_rate, filename)
+        data = data.astype(np.float32)
+        data /= np.max(np.abs(data))
+        text = transcriber({"sampling_rate": sample_rate, "raw": data})["text"]
+        gr.Info(f"Transcribed text is: {text}\nProcessing the input...")
+
+    except Exception as e:
+        logger.error(f"Error: {e}")
+        raise Exception("Error transcribing audio.")
+    return text

llama2.py

@@ -1,86 +0,0 @@
-# -*- coding: utf-8 -*-
-"""llama2
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/15UK6iHd1y0pMMQc-DbZIYhSteoTCUsMH
-"""
-
-#install and then restart execution
-!pip install accelerate
-!pip install bitsandbytes
-!pip install optimum
-!pip install auto-gptq
-
-!pip install transformers
-from transformers import AutoModelForCausalLM,AutoTokenizer
-import torch
-!pip install transformers huggingface_hub
-from huggingface_hub import notebook_login
-
-notebook_login()
-
-mn = 'stabilityai/StableBeluga-7B'
-#mn = "TheBloke/Llama-2-7b-Chat-GPTQ"
-
-model = AutoModelForCausalLM.from_pretrained(mn, device_map=0, load_in_8bit=True)
-
-#model = AutoModelForCausalLM.from_pretrained(mn, device_map=0, torch_dtype=torch.float16)
-
-sb_sys = "### System:\nYou are a AI driving assistant in my car, that follows instructions extremely well. Help as much as you can.\n\n"
-
-def gen(p, maxlen=15, sample=True):
-    toks = tokr(p, return_tensors="pt")
-    res = model.generate(**toks.to("cuda"), max_new_tokens=maxlen, do_sample=sample).to('cpu')
-    return tokr.batch_decode(res)
-
-tokr = AutoTokenizer.from_pretrained(mn)
-
-#to have a prompt corresponding to the specific format required by the fine-tuned model Stable Beluga
-def mk_prompt(user, syst=sb_sys): return f"{syst}### User: {user}\n\n### Assistant:\n"
-
-complete_answer= ''
-
-#attempt to get user location
-
-import requests
-
-response = requests.get("http://ip-api.com/json/")
-data = response.json()
-print(data['city'], data['lat'], data['lon'])
-city= data['city']
-lat = data['lat']
-lon = data['lon']
-
-import re
-model_answer= ''
-general_context= f'I am in my car in {city}, latitude {lat}, longitude {lon}, I can move with my car to reach a destination'
-pattern = r"Assistant:\\n(.*?)</s>"
-
-ques = "I hate pizzas"
-
-ques_ctx = f"""Answer the question with the help of the provided context.
-
-## Context
-
-{general_context} .
-
-## Question
-
-{ques}"""
-
-complete_answer = str(gen(mk_prompt(ques_ctx), 150))
-
-match = re.search(pattern, complete_answer, re.DOTALL)
-
-if match:
-    # Extracting the text
-    model_answer = match.group(1)
-else:
-    model_answer = "There has been an error with the generated response."
-
-general_context +=  model_answer
-print(model_answer)
-
-print(complete_answer)

main.py

@@ -1,24 +1,14 @@
-import time
-
 import gradio as gr
-import numpy as np
-import ollama
-import torch
-import torchaudio
-import typer
 from langchain.memory import ChatMessageHistory
 from langchain.tools import tool
-from langchain.tools.base import StructuredTool
 from langchain_core.utils.function_calling import convert_to_openai_tool
 from loguru import logger
-from transformers import pipeline
 
 from kitt.core import tts_gradio
 from kitt.core import utils as kitt_utils
 from kitt.core import voice_options
-
-# from kitt.core.model import process_query
 from kitt.core.model import generate_function_call as process_query
+from kitt.core.stt import save_and_transcribe_audio
 from kitt.core.tts import prep_for_tts, run_melo_tts, run_tts_fast, run_tts_replicate
 from kitt.skills import (
     code_interpreter,
@@ -34,7 +24,6 @@ from kitt.skills import (
     set_vehicle_destination,
     set_vehicle_speed,
 )
-from kitt.skills import vehicle_status as vehicle_status_fn
 from kitt.skills.common import config, vehicle
 from kitt.skills.routing import calculate_route, find_address
 
@@ -65,54 +54,6 @@ global_context = {
 speaker_embedding_cache = {}
 history = ChatMessageHistory()
 
-MODEL_FUNC = "nexusraven"
-MODEL_GENERAL = "llama3:instruct"
-
-RAVEN_PROMPT_FUNC = """You are a helpful AI assistant in a car (vehicle), that follows instructions extremely well. \
-Answer questions concisely and do not mention what you base your reply on."
-
-{raven_tools}
-
-{history}
-
-User Query: Question: {input}<human_end>
-"""
-
-
-HERMES_PROMPT_FUNC = """
-<|im_start|>system
-You are a helpful AI assistant in a car (vehicle), that follows instructions extremely well. \
-Answer questions concisely and do not mention what you base your reply on.<|im_end|>
-<|im_start|>user
-{{ .Prompt }}<|im_end|>
-<|im_start|>assistant
-"""
-
-
-def get_prompt(template, input, history, tools):
-    # "vehicle_status": vehicle_status_fn()[0]
-    kwargs = {"history": history, "input": input}
-    prompt = "<human>:\n"
-    for tool in tools:
-        func_signature, func_docstring = tool.description.split(" - ", 1)
-        prompt += f'Function:\n<func_start>def {func_signature}<func_end>\n<docstring_start>\n"""\n{func_docstring}\n"""\n<docstring_end>\n'
-    kwargs["raven_tools"] = prompt
-
-    if history:
-        kwargs["history"] = f"Previous conversation history:{history}\n"
-
-    return template.format(**kwargs).replace("{{", "{").replace("}}", "}")
-
-
-def use_tool(func_name, kwargs, tools):
-    for tool in tools:
-        if tool.name == func_name:
-            return tool.invoke(input=kwargs)
-    return None
-
-
-# llm = Ollama(model="nexusraven", stop=["\nReflection:", "\nThought:"], keep_alive=60*10)
-
 
 # Generate options for hours (00-23)
 hour_options = [f"{i:02d}:00:00" for i in range(24)]
@@ -136,20 +77,6 @@ def set_time(time_picker):
     return vehicle
 
 
-tools = [
-    # StructuredTool.from_function(get_weather),
-    # StructuredTool.from_function(find_route),
-    # StructuredTool.from_function(vehicle_status_fn),
-    # StructuredTool.from_function(set_vehicle_speed),
-    # StructuredTool.from_function(set_vehicle_destination),
-    # StructuredTool.from_function(search_points_of_interest),
-    # StructuredTool.from_function(search_along_route),
-    # StructuredTool.from_function(date_time_info),
-    # StructuredTool.from_function(get_weather_current_location),
-    # StructuredTool.from_function(code_interpreter),
-    # StructuredTool.from_function(do_anything_else),
-]
-
 functions = [
     # set_vehicle_speed,
     set_vehicle_destination,
@@ -161,59 +88,11 @@ functions = [
 openai_tools = [convert_to_openai_tool(tool) for tool in functions]
 
 
-def run_generic_model(query):
-    print(f"Running the generic model with query: {query}")
-    data = {
-        "prompt": f"Answer the question below in a short and concise manner.\n{query}",
-        "model": MODEL_GENERAL,
-        "options": {
-            # "temperature": 0.1,
-            # "stop":["\nReflection:", "\nThought:"]
-        },
-    }
-    out = ollama.generate(**data)
-    return out["response"]
-
-
 def clear_history():
     logger.info("Clearing the conversation history...")
     history.clear()
 
 
-def run_nexusraven_model(query, voice_character, state):
-    global_context["prompt"] = get_prompt(RAVEN_PROMPT_FUNC, query, "", tools)
-    print("Prompt: ", global_context["prompt"])
-    data = {
-        "prompt": global_context["prompt"],
-        # "streaming": False,
-        "model": "nexusraven",
-        # "model": "smangrul/llama-3-8b-instruct-function-calling",
-        "raw": True,
-        "options": {"temperature": 0.5, "stop": ["\nReflection:", "\nThought:"]},
-    }
-    out = ollama.generate(**data)
-    llm_response = out["response"]
-    if "Call: " in llm_response:
-        print(f"llm_response: {llm_response}")
-        llm_response = llm_response.replace("<bot_end>", " ")
-        func_name, kwargs = extract_func_args(llm_response)
-        print(f"Function: {func_name}, Args: {kwargs}")
-        if func_name == "do_anything_else":
-            output_text = run_generic_model(query)
-        else:
-            output_text = use_tool(func_name, kwargs, tools)
-    else:
-        output_text = out["response"]
-
-    if type(output_text) == tuple:
-        output_text = output_text[0]
-    gr.Info(f"Output text: {output_text}\nGenerating voice output...")
-    return (
-        output_text,
-        tts_gradio(output_text, voice_character, speaker_embedding_cache)[0],
-    )
-
-
 def run_llama3_model(query, voice_character, state):
 
     assert len(functions) > 0, "No functions to call"
@@ -249,18 +128,13 @@ def run_llama3_model(query, voice_character, state):
 
 
 def run_model(query, voice_character, state):
-    model = state.get("model", "nexusraven")
+    model = state.get("model", "llama3")
     query = query.strip().replace("'", "")
     logger.info(
         f"Running model: {model} with query: {query}, voice_character: {voice_character} and llm_backend: {state['llm_backend']}, tts_enabled: {state['tts_enabled']}"
     )
     global_context["query"] = query
-    if model == "nexusraven":
-        text, voice = run_nexusraven_model(query, voice_character, state)
-    elif model == "llama3":
-        text, voice = run_llama3_model(query, voice_character, state)
-    else:
-        text, voice = "Error running model", None
+    text, voice = run_llama3_model(query, voice_character, state)
 
     if not state["enable_history"]:
         history.clear()
@@ -308,44 +182,6 @@ def update_vehicle_status(trip_progress, origin, destination, state):
     return vehicle, plot, state
 
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-transcriber = pipeline(
-    "automatic-speech-recognition", model="openai/whisper-base.en", device=device
-)
-
-
-def save_audio_as_wav(data, sample_rate, file_path):
-    # make a tensor from the numpy array
-    data = torch.tensor(data).reshape(1, -1)
-    torchaudio.save(
-        file_path, data, sample_rate=sample_rate, bits_per_sample=16, encoding="PCM_S"
-    )
-
-
-def save_and_transcribe_audio(audio):
-    try:
-        # capture the audio and save it to a file as wav or mp3
-        # file_name = save("audioinput.wav")
-        sr, y = audio
-        # y = y.astype(np.float32)
-        # y /= np.max(np.abs(y))
-
-        # add timestamp to file name
-        filename = f"recordings/audio{time.time()}.wav"
-        save_audio_as_wav(y, sr, filename)
-
-        sr, y = audio
-        y = y.astype(np.float32)
-        y /= np.max(np.abs(y))
-        text = transcriber({"sampling_rate": sr, "raw": y})["text"]
-        gr.Info(f"Transcribed text is: {text}\nProcessing the input...")
-
-    except Exception as e:
-        logger.error(f"Error: {e}")
-        raise Exception("Error transcribing audio.")
-    return text
-
-
 def save_and_transcribe_run_model(audio, voice_character, state):
     text = save_and_transcribe_audio(audio)
     out_text, out_voice, vehicle_status, state, update_proxy = run_model(
@@ -494,7 +330,12 @@ def create_demo(tts_server: bool = False, model="llama3"):
                     0, 100, step=5, label="Trip progress", interactive=True
                 )
 
-                # map_if = gr.Interface(fn=plot_map, inputs=year_input, outputs=map_plot)
+            # with gr.Column(scale=1, min_width=300):
+            #     gr.Image("linkedin-1.png", label="Linkedin - Sasan Jafarnejad")
+            #     gr.Image(
+            #         "team-ubix.png",
+            #         label="Research Team - UBIX - University of Luxembourg",
+            #     )
 
         with gr.Row():
             with gr.Column():
@@ -647,30 +488,3 @@ demo.launch(
     ssl_verify=False,
     share=False,
 )
-
-app = typer.Typer()
-
-
-@app.command()
-def run(tts_server: bool = False):
-    global demo
-    demo = create_demo(tts_server)
-    demo.launch(
-        debug=True, server_name="0.0.0.0", server_port=7860, ssl_verify=True, share=True
-    )
-
-
-@app.command()
-def dev(tts_server: bool = False, model: str = "llama3"):
-    demo = create_demo(tts_server, model)
-    demo.launch(
-        debug=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        ssl_verify=False,
-        share=False,
-    )
-
-
-if __name__ == "__main__":
-    app()

stt.ipynb

@@ -1,292 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/homebrew/Caskroom/miniconda/base/envs/llm/lib/python3.11/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n",
-      "/opt/homebrew/Caskroom/miniconda/base/envs/llm/lib/python3.11/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n",
-      "/opt/homebrew/Caskroom/miniconda/base/envs/llm/lib/python3.11/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n"
-     ]
-    }
-   ],
-   "source": [
-    "#STT (speech to text)\n",
-    "from transformers import WhisperProcessor, WhisperForConditionalGeneration\n",
-    "from transformers import pipeline\n",
-    "import librosa"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# load model and processor for speech-to-text\n",
-    "processor = WhisperProcessor.from_pretrained(\"openai/whisper-small\")\n",
-    "modelw = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-small\")\n",
-    "# modelw.config.forced_decoder_ids = None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
-     ]
-    }
-   ],
-   "source": [
-    "transcriber = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base.en\", device=device)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cpu')"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transcriber.device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "audio = \"/Users/sasan.jafarnejad/dev/uni/talking-car/audio1713724521.779008.wav\"\n",
-    "audio = \"/Users/sasan.jafarnejad/dev/uni/talking-car/audio/attenborough/neutral.wav\"\n",
-    "if type(audio) == str:\n",
-    "    link_to_audio = audio\n",
-    "    audio = torchaudio.load(link_to_audio)\n",
-    "    audio = audio[0].squeeze().numpy(), sr\n",
-    "    if len(audio[0].shape) == 2:\n",
-    "        audio = audio[0].mean(axis=0), audio[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y, sr = audio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "out = transcriber({\"sampling_rate\": sr, \"raw\":y})[\"text\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\" If you speed up time, plants begin to reveal their true nature. They're not passive organisms, as you might think, but competitive creatures every bit as aggressive as animals. They're locked in a desperate battle for light and space. They stretch and pulse as they strive to barge their way into pole position. Creepers and vines reach around for the branch or stem of another plant on which to hitch a ride. This is an assassin bug. To us, it's easy enough to spot because it moves. To its prey, that's irrelevant, because it smells like one of their number. The assassin sucks its victims dry and blues their empty husks onto its back. This one is already carrying at least 20 corpses. Its irregular shape makes it hard for other predators to spot it and makes it virtually invisible to its prey, ants. It enters this ant colony unchallenged. Its coat of ant corpses masks its own odour. To the ants, it smells like one of their own, and that's what matters. They'll even run straight over the top of it. The assassin simply takes an ant whenever it feels hungry, and the body of each victim then adds to its disguise. The giants here too. This is the Moala Moala, the sunfish. It's huge, three meters across, and addicted to lying on its side at the surface. It eats vast quantities of jellyfish. And there are not only fish from me in these waters, there are mammals. sea lions, whose ancestors originally came from the coasts of California. The Galapagos plankton is so abundant it attracts some of the biggest of all ocean mammals, humpback whales, and rivalling them in size the biggest of all fish. 20 ton whale shark. Few parts of the world's oceans can equally as Galapagos waters for sheer variety and abundance. That creature was a penguin. Penguins are ocean-goings for moose. But a few thousand years ago some of them got caught in the cold waters of the Humboldt current and were carried northwards up the coast of South America and out to the Galapagos. They could hardly have found anywhere more different from their polar home and in and the response they chain, the Emperor penguin that lives near the South Pole stands over a metre high, Galapagos penguin is now only half, and that helps a lot in the Galapagos. Small animals lose heat much faster than big ones, and the penguins have developed behavioral tricks as well. Bear feet are easily sunburnt, so they do their best to keep them covered, and some parts of the sea around the islands are quite cool. The humbalt current flowing up from the Antarctic and washing around the western parts of the archipelago is still quite chilly. So most of the penguins stay in the channel between the two westernmost island and when things get really hot, they can still cool off with the swim. They're quick to detect the slightest variation in temperature and move around to find places where an eddy might have brought a pleasing chill. The arrival of penguins must be the most unlikely event in the whole story of the colonization of the Galapagos. The existence of creatures like these so far from the nearest continent poses many questions. How, for example, did these enormous beasts get to the islands in the first place? But perhaps the most extraordinary thing about the Galapagos tortoises is that they're not all the same. islands have different kinds. In the heyday there were 15 species. They seem to have appeared in an evolutionary blink of the eye. But will soon become a leaf. It's no ordinary. It has a special altogether more sinister. This is Nepenthes, the pitcher plant. It grows in nutrient poor soils, So has to find nitrogen and minerals in another way. The leaf, just like a flower, attracts insects with a reward. The pitcher is coloured and scented to appeal to flies looking for a meal of rotting flesh. The visitors are rewarded with a greasy substance on the underside of the pitcher's lid. But the plant wants something in return, not pollen, but a meal. The lip of the pitcher is covered in tiny slippery ridges. Wax lubricates the surface further. It's extremely difficult to hold on, even for a fly. Once inside, there's no escape. The leaf holds a pool of digestive liquid. This contains microscopic elastic filaments, which give it the properties of quicksand. The more the insect struggles, the deeper it sinks. Enzymes begin to dissolve the victim's body while it's still alive.\""
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def transcript(audio):\n",
-    "    if type(audio) == str:\n",
-    "        link_to_audio = audio\n",
-    "        audio = torchaudio.load(link_to_audio)\n",
-    "\n",
-    "    # We assume that the audio is the audio tensor\n",
-    "\n",
-    "    # process the audio array\n",
-    "    input_features = processor(audio_array, sampling_rate, return_tensors=\"pt\").input_features\n",
-    "    predicted_ids = modelw.generate(input_features)\n",
-    "\n",
-    "    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\n",
-    "    \n",
-    "    return audio_path, state['context'], state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def transcript(general_context, link_to_audio, voice, place, time, delete_history, state):\n",
-    "    \"\"\"this function manages speech-to-text to input Fnanswer function and text-to-speech with the Fnanswer output\"\"\"\n",
-    "    # load audio from a specific path\n",
-    "    audio_path = link_to_audio\n",
-    "    audio_array, sampling_rate = librosa.load(link_to_audio, sr=16000)  # \"sr=16000\" ensures that the sampling rate is as required\n",
-    "\n",
-    "    # process the audio array\n",
-    "    input_features = processor(audio_array, sampling_rate, return_tensors=\"pt\").input_features\n",
-    "    predicted_ids = modelw.generate(input_features)\n",
-    "\n",
-    "    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\n",
-    "    \n",
-    "\n",
-    "\n",
-    "    return audio_path, state['context'], state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7875\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7875/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import gradio as gr\n",
-    "import torchaudio\n",
-    "import time\n",
-    "import torch\n",
-    "\n",
-    "def save_audio_as_wav(data, sample_rate, file_path):\n",
-    "    # make a tensor from the numpy array\n",
-    "    data = torch.tensor(data).reshape(1, -1)\n",
-    "    torchaudio.save(file_path, data, sample_rate=sample_rate, bits_per_sample=16, encoding=\"PCM_S\")\n",
-    "\n",
-    "def save_and_transcribe_audio(audio):\n",
-    "    # capture the audio and save it to a file as wav or mp3\n",
-    "    # file_name = save(\"audioinput.wav\")\n",
-    "    sr, y = audio\n",
-    "    # y = y.astype(np.float32)\n",
-    "    # y /= np.max(np.abs(y))\n",
-    "\n",
-    "    # add timestamp to file name\n",
-    "    filename = f\"audio{time.time()}.wav\"\n",
-    "    save_audio_as_wav(y, sr, filename)\n",
-    "    \n",
-    "    sr, y = audio\n",
-    "    y = y.astype(np.float32)\n",
-    "    y /= np.max(np.abs(y))\n",
-    "    text = transcriber({\"sampling_rate\": sr, \"raw\":y})[\"text\"]\n",
-    "    return text\n",
-    "\n",
-    "gr.Interface(\n",
-    "    fn=save_and_transcribe_audio, \n",
-    "    inputs=gr.Audio(sources=\"microphone\", type=\"numpy\", label=\"Record Audio\"), \n",
-    "    outputs=\"text\").launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "llm",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

stttotts.py

@@ -1,177 +0,0 @@
-# -*- coding: utf-8 -*-
-"""sttToTts.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/15QqRKFSwfhRdnaj5-R1z6xFfeEOOta38
-"""
-
-#text-to-speech and speech to text
-!pip install TTS
-!pip install transformers
-
-#text to speech
-from TTS.api import TTS
-tts = TTS("tts_models/multilingual/multi-dataset/your_tts", cs_api_model  = "TTS.cs_api.CS_API", gpu=True)
-
-#voice recording
-import IPython.display
-import google.colab.output
-import base64
-# all imports for voice recording
-from IPython.display import Javascript
-from google.colab import output
-from base64 import b64decode
-
-#to record sound, found on https://gist.github.com/korakot/c21c3476c024ad6d56d5f48b0bca92be
-
-RECORD = """
-const sleep  = time => new Promise(resolve => setTimeout(resolve, time))
-const b2text = blob => new Promise(resolve => {
-  const reader = new FileReader()
-  reader.onloadend = e => resolve(e.srcElement.result)
-  reader.readAsDataURL(blob)
-})
-var record = time => new Promise(async resolve => {
-  stream = await navigator.mediaDevices.getUserMedia({ audio: true })
-  recorder = new MediaRecorder(stream)
-  chunks = []
-  recorder.ondataavailable = e => chunks.push(e.data)
-  recorder.start()
-  await sleep(time)
-  recorder.onstop = async ()=>{
-    blob = new Blob(chunks)
-    text = await b2text(blob)
-    resolve(text)
-  }
-  recorder.stop()
-})
-"""
-
-def record(name, sec):
-  display(Javascript(RECORD))
-  s = output.eval_js('record(%d)' % (sec*1000))
-  b = b64decode(s.split(',')[1])
-  with open(f'{name}.webm','wb') as f:
-    f.write(b)
-  return (f'{name}.webm')  # or webm ?
-
-#to record the text which is going to be transcribed
-record('audio', sec = 10)
-
-#works -- speech-to-text with an audio I provide the path to reach
-from transformers import WhisperProcessor, WhisperForConditionalGeneration
-import librosa
-
-# load model and processor
-processor = WhisperProcessor.from_pretrained("openai/whisper-small")
-model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
-model.config.forced_decoder_ids = None
-
-# load audio from a specific path
-audio_path = "audio.webm"
-audio_array, sampling_rate = librosa.load(audio_path, sr=16000)  # "sr=16000" ensures that the sampling rate is as required
-
-
-# process the audio array
-input_features = processor(audio_array, sampling_rate, return_tensors="pt").input_features
-
-
-predicted_ids = model.generate(input_features)
-
-transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-print(transcription)
-
-#to record the speaker's voice used for tts
-record('speaker', sec = 10 )
-
-#library to convert digits to words (ex : 1 --> one)
-import locale
-locale.getpreferredencoding = lambda: "UTF-8"
-!pip install inflect
-
-import re
-import inflect
-#because numbers under digit format are ignored otherwise
-def convert_numbers_to_words(s):
-    p = inflect.engine()
-    # Find all sequences of digits in the string
-    numbers = re.findall(r'\d+', s)
-    for number in numbers:
-        # Convert each number to words
-        words = p.number_to_words(number)
-        # Replace the original number in the string with its word representation
-        s = s.replace(number, words)
-    return s
-
-#model test 1 for text to speech
-#works - text to speech with voice cloner (by providing the path to the audio where the voice is)
-from google.colab import drive
-from IPython.display import Audio
-
-
-
-tts.tts_to_file(text=convert_numbers_to_words(str(transcription)),
-                file_path="output.wav",
-                speaker_wav='speaker.webm',
-                language="en",
-                emotion ='angry',
-                speed = 2)
-audio_path = "output.wav"
-Audio(audio_path)
-
-#model test 2 for text to speech
-from IPython.display import Audio
-# TTS with on the fly voice conversion
-api = TTS("tts_models/deu/fairseq/vits")
-api.tts_with_vc_to_file(
-    text="Wie sage ich auf Italienisch, dass ich dich liebe?",
-    speaker_wav="speaker.webm",
-    file_path="ouptut.wav"
-)
-audio_path = "output.wav"
-Audio(audio_path)
-
-#model test 3 for text to speech
-from TTS.api import TTS
-tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1", gpu=True)
-
-from IPython.display import Audio
-
-
-# generate speech by cloning a voice using custom settings
-tts.tts_to_file(text="But for me to rap like a computer it must be in my genes I got a laptop in my back pocket My pen'll go off when I half-cock it Got a fat knot from that rap profit Made a livin' and a killin' off it Ever since Bill Clinton was still in office with Monica Lewinsky feelin' on his nutsack I'm an MC still as honest",
-                file_path="output.wav",
-                speaker_wav="Slide 1.m4a",
-                language="en",
-                emotion = "neutral",
-                decoder_iterations=35)
-
-audio_path = "output.wav"
-Audio(audio_path)
-
-# Init TTS with the target studio speaker
-tts = TTS(model_name="coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar=False)
-# Run TTS
-tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH)
-# Run TTS with emotion and speed control
-tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH, emotion="Happy", speed=1.5)
-
-#model test 4 for text to speech
-from IPython.display import Audio
-
-from TTS.api import TTS
-#api = TTS(model_name="tts_models/eng/fairseq/vits").to("cuda")
-#api.tts_to_file("This is a test.", file_path="output.wav")
-
-# TTS with on the fly voice conversion
-api = TTS("tts_models/deu/fairseq/vits")
-api.tts_with_vc_to_file(
-    "I am a basic human",
-    speaker_wav="speaker.webm",
-    file_path="output.wav"
-)
-
-audio_path = "output.wav"
-Audio(audio_path)

test_raven.ipynb

@@ -1,936 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### libraries import"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true,
-    "id": "oOnNfKjX4IAV"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n",
-      "/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n",
-      "/opt/conda/lib/python3.10/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
-      "  _torch_pytree._register_pytree_node(\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "#gradio interface\n",
-    "import gradio as gr\n",
-    "\n",
-    "from transformers import AutoModelForCausalLM,AutoTokenizer\n",
-    "import torch\n",
-    "\n",
-    "#STT (speech to text)\n",
-    "from transformers import WhisperProcessor, WhisperForConditionalGeneration\n",
-    "import librosa\n",
-    "\n",
-    "#TTS (text to speech)\n",
-    "import torch\n",
-    "from TTS.api import TTS\n",
-    "from IPython.display import Audio\n",
-    "\n",
-    "#json request for APIs\n",
-    "import requests\n",
-    "import json\n",
-    "\n",
-    "#regular expressions\n",
-    "import re\n",
-    "\n",
-    "#langchain and function calling\n",
-    "from typing import List, Literal, Union\n",
-    "import requests\n",
-    "from functools import partial\n",
-    "import math\n",
-    "\n",
-    "\n",
-    "#langchain, not used anymore since I had to find another way fast to stop using the endpoint, but could be interesting to reuse \n",
-    "from langchain.tools.base import StructuredTool\n",
-    "from langchain.agents import (\n",
-    "    Tool,\n",
-    "    AgentExecutor,\n",
-    "    LLMSingleActionAgent,\n",
-    "    AgentOutputParser,\n",
-    ")\n",
-    "from langchain.schema import AgentAction, AgentFinish, OutputParserException\n",
-    "from langchain.prompts import StringPromptTemplate\n",
-    "from langchain.llms import HuggingFaceTextGenInference\n",
-    "from langchain.chains import LLMChain\n",
-    "\n",
-    "\n",
-    "\n",
-    "from datetime import datetime, timedelta, timezone\n",
-    "from transformers import pipeline\n",
-    "import inspect"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from apis import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Models loads"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
-      "Loading checkpoint shards: 100%|██████████| 3/3 [00:10<00:00,  3.45s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "#NexusRaven for function calling\n",
-    "model_id = \"Nexusflow/NexusRaven-13B\"\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
-    "modelNexus = AutoModelForCausalLM.from_pretrained(model_id, device_map=0, load_in_4bit=True)\n",
-    "pipe = pipeline(\"text-generation\", model=modelNexus, tokenizer = tokenizer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Function calling with NexusRaven "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load api key from .env file\n",
-    "# weather api and tomtom api key\n",
-    "from dotenv import load_dotenv\n",
-    "load_dotenv()\n",
-    "WHEATHER_API_KEY = os.getenv(\"WEATHER_API_KEY\")\n",
-    "TOMTOM_KEY = os.getenv(\"TOMTOM_API_KEY\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#FUNCTION CALLING \n",
-    "\n",
-    "##########################################################\n",
-    "# Step 1: Define the functions you want to articulate. ###\n",
-    "##########################################################\n",
-    "\n",
-    "# apis.py\n",
-    "\n",
-    "\n",
-    "#############################################################\n",
-    "# Step 2: Let's define some utils for building the prompt ###\n",
-    "#############################################################\n",
-    "\n",
-    "\n",
-    "def format_functions_for_prompt(*functions):\n",
-    "    formatted_functions = []\n",
-    "    for func in functions:\n",
-    "        source_code = inspect.getsource(func)\n",
-    "        # Get function name\n",
-    "        function_name = func.__name__\n",
-    "        # Get function signature\n",
-    "        signature = inspect.signature(func)\n",
-    "        docstring = inspect.getdoc(func)\n",
-    "        formatted_functions.append(\n",
-    "            f\"OPTION:\\n<func_start>{function_name}{signature}<func_end>\\n<docstring_start>\\n{docstring}\\n<docstring_end>\"\n",
-    "        )\n",
-    "    return \"\\n\".join(formatted_functions)\n",
-    "\n",
-    "\n",
-    "##############################\n",
-    "# Step 3: Construct Prompt ###\n",
-    "##############################\n",
-    "\n",
-    "\n",
-    "def construct_prompt(user_query: str, context):\n",
-    "    formatted_prompt = format_functions_for_prompt(get_weather, find_points_of_interest, find_route, get_forecast, search_along_route)\n",
-    "    formatted_prompt += f'\\n\\nContext : {context}'\n",
-    "    formatted_prompt += f\"\\n\\nUser Query: Question: {user_query}\\n\"\n",
-    "\n",
-    "    prompt = (\n",
-    "        \"<human>:\\n\"\n",
-    "        + formatted_prompt\n",
-    "        + \"Please pick a function from the above options that best answers the user query and fill in the appropriate arguments.<human_end>\"\n",
-    "    )\n",
-    "    return prompt\n",
-    "\n",
-    "#######################################\n",
-    "# Step 4: Execute the function call ###\n",
-    "#######################################\n",
-    "\n",
-    "\n",
-    "def execute_function_call(model_output):\n",
-    "    # Ignore everything after \"Reflection\" since that is not essential.\n",
-    "    function_call = (\n",
-    "        model_output[0][\"generated_text\"]\n",
-    "        .strip()\n",
-    "        .split(\"\\n\")[1]\n",
-    "        .replace(\"Initial Answer:\", \"\")\n",
-    "        .strip()\n",
-    "    )\n",
-    "\n",
-    "    try:\n",
-    "        return eval(function_call)\n",
-    "    except Exception as e:\n",
-    "        return str(e)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# convert bytes to megabytes\n",
-    "def get_cuda_usage(): return round(torch.cuda.memory_allocated(\"cuda:0\")/1024/1024,2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = construct_prompt(\"What restaurants are there on the road from Luxembourg Gare, which coordinates are lat 49.5999681, lon 6.1342493, to Thionville?\", \"\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<human>:\n",
-      "OPTION:\n",
-      "<func_start>get_weather(city_name: str = '', **kwargs)<func_end>\n",
-      "<docstring_start>\n",
-      "Returns the CURRENT weather in a specified city.\n",
-      "Args:\n",
-      "city_name (string) : Required. The name of the city.\n",
-      "<docstring_end>\n",
-      "OPTION:\n",
-      "<func_start>find_points_of_interest(lat='0', lon='0', city='', type_of_poi='restaurant', **kwargs)<func_end>\n",
-      "<docstring_start>\n",
-      "Return some of the closest points of interest for a specific location and type of point of interest. The more parameters there are, the more precise.\n",
-      ":param lat (string):  latitude\n",
-      ":param lon (string):  longitude\n",
-      ":param city (string): Required. city\n",
-      ":param type_of_poi (string): Required. type of point of interest depending on what the user wants to do.\n",
-      "<docstring_end>\n",
-      "OPTION:\n",
-      "<func_start>find_route(lat_depart='0', lon_depart='0', city_depart='', address_destination='', depart_time='', **kwargs)<func_end>\n",
-      "<docstring_start>\n",
-      "Return the distance and the estimated time to go to a specific destination from the current place, at a specified depart time.\n",
-      ":param lat_depart (string):  latitude of depart\n",
-      ":param lon_depart (string):  longitude of depart\n",
-      ":param city_depart (string): Required. city of depart\n",
-      ":param address_destination (string): Required. The destination\n",
-      ":param depart_time (string):  departure hour, in the format '08:00:20'.\n",
-      "<docstring_end>\n",
-      "OPTION:\n",
-      "<func_start>get_forecast(city_name: str = '', when=0, **kwargs)<func_end>\n",
-      "<docstring_start>\n",
-      "Returns the weather forecast in a specified number of days for a specified city .\n",
-      "Args:\n",
-      "city_name (string) : Required. The name of the city.\n",
-      "when (int) : Required. in number of days (until the day for which we want to know the forecast) (example: tomorrow is 1, in two days is 2, etc.)\n",
-      "<docstring_end>\n",
-      "OPTION:\n",
-      "<func_start>search_along_route(latitude_depart, longitude_depart, city_destination, type_of_poi)<func_end>\n",
-      "<docstring_start>\n",
-      "Return some of the closest points of interest along the route from the depart point, specified by its coordinates and a city destination.\n",
-      ":param latitude_depart (string):  Required. Latitude of depart location\n",
-      ":param longitude_depart (string):  Required. Longitude of depart location\n",
-      ":param city_destination (string): Required. City destination\n",
-      ":param type_of_poi (string): Required. type of point of interest depending on what the user wants to do.\n",
-      "<docstring_end>\n",
-      "\n",
-      "Context : \n",
-      "\n",
-      "User Query: Question: What restaurants are there on the road from Luxembourg Gare, which coordinates are lat 49.5999681, lon 6.1342493, to Thionville?\n",
-      "Please pick a function from the above options that best answers the user query and fill in the appropriate arguments.<human_end>\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_output = pipe(\n",
-    "    prompt, do_sample=False, max_new_tokens=3000, return_full_text=False\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " \n",
-      " Thought: The purpose of the def search_along_route(latitude_depart, longitude_depart, city_destination, type_of_poi) is to return some of the closest points of interest along the route from the depart point, specified by its coordinates and a city destination.\n",
-      "Initial Answer: search_along_route(49.5999681, 6.1342493, 'Thionville','restaurant')\n",
-      "Reflection: The search_along_route function takes in four arguments: latitude_depart, longitude_depart, city_destination, and type_of_poi.\n",
-      "\n",
-      "The user has asked what restaurants are there on the road from Luxembourg Gare, which coordinates are lat 49.5999681, lon 6.1342493, to Thionville.\n",
-      "\n",
-      "The call provided is search_along_route(49.5999681, 6.1342493, 'Thionville','restaurant').\n",
-      "\n",
-      "The call can be improved because the function requires the latitude and longitude of the depart point, which are not provided.\n",
-      "\n",
-      "The correct call would be search_along_route(latitude_depart = 49.5999681, longitude_depart = 6.1342493, city_destination = 'Thionville', type_of_poi ='restaurant').\n",
-      "\n",
-      "This call would return some of the closest points of interest along the route from the depart point, specified by its coordinates and a city destination.\n",
-      "\n",
-      "Therefore, the argument chosen for the latitude_depart and longitude_depart is 49.5999681 and 6.1342493, respectively.\n",
-      "\n",
-      "This call can be run:\n",
-      "Fixed Call: search_along_route(latitude_depart = 49.5999681, longitude_depart = 6.1342493, city_destination = 'Thionville', type_of_poi ='restaurant')\n",
-      "Fixed Function Name: search_along_route\n",
-      "Fixed Function Args: {\"latitude_depart\" : 49.5999681, \"longitude_depart\" : 6.1342493, \"city_destination\" : 'Thionville', \"type_of_poi\" :'restaurant'}\n",
-      "\n",
-      "Done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(model_output[0][\"generated_text\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "before everything: 7802.67\n",
-      "after creating prompt: 7802.67\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:391: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
-      "  warnings.warn('Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.')\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " \n",
-      " Thought: The purpose of the def search_along_route(latitude_depart, longitude_depart, city_destination, type_of_poi) is to return some of the closest points of interest along the route from the depart point, specified by its coordinates and a city destination.\n",
-      "Initial Answer: search_along_route(49.5999681, 6.1342493, 'Thionville','restaurant')\n",
-      "Reflection: The search_along_route function takes in four arguments: latitude_depart, longitude_depart, city_destination, and type_of_poi.\n",
-      "\n",
-      "The user has asked what restaurants are there on the road from Luxembourg Gare, which coordinates are lat 49.5999681, lon 6.1342493, to Thionville.\n",
-      "\n",
-      "The call provided is search_along_route(49.5999681, 6.1342493, 'Thionville','restaurant').\n",
-      "\n",
-      "The call can be improved because the function requires the latitude and longitude of the depart point, as well as the city destination. The call provided only provides the latitude and longitude of the depart point, and the city destination.\n",
-      "\n",
-      "The correct call would be\n"
-     ]
-    }
-   ],
-   "source": [
-    "# might be deleted\n",
-    "# Compute a Simple equation\n",
-    "print(f\"before everything: {get_cuda_usage()}\")\n",
-    "prompt = construct_prompt(\"What restaurants are there on the road from Luxembourg Gare, which coordinates are lat 49.5999681, lon 6.1342493, to Thionville?\", \"\")\n",
-    "print(f\"after creating prompt: {get_cuda_usage()}\")\n",
-    "model_output = pipe(\n",
-    "    prompt, do_sample=False, max_new_tokens=300, return_full_text=False\n",
-    "    )\n",
-    "print(model_output[0][\"generated_text\"])\n",
-    "#execute_function_call(pipe(construct_prompt(\"Is it raining in Belval, ?\"), do_sample=False, max_new_tokens=300, return_full_text=False))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "creating the pipe of model output: 13736.26\n",
-      "49.3579272\n",
-      "after execute function call: 13736.26\n",
-      "after garbage collect and empty_cache: 13736.26\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"creating the pipe of model output: {get_cuda_usage()}\")\n",
-    "result = execute_function_call(model_output)\n",
-    "print(f\"after execute function call: {get_cuda_usage()}\")\n",
-    "del model_output\n",
-    "import gc         # garbage collect library\n",
-    "gc.collect()\n",
-    "torch.cuda.empty_cache() \n",
-    "print(f\"after garbage collect and empty_cache: {get_cuda_usage()}\")\n",
-    "#print(\"Model Output:\", model_output)\n",
-    "# print(\"Execution Result:\", result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## functions to process the anwser and the question"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#generation of text with Stable beluga \n",
-    "def gen(p, maxlen=15, sample=True):\n",
-    "    toks = tokr(p, return_tensors=\"pt\")\n",
-    "    res = model.generate(**toks.to(\"cuda\"), max_new_tokens=maxlen, do_sample=sample).to('cpu')\n",
-    "    return tokr.batch_decode(res)\n",
-    "\n",
-    "#to have a prompt corresponding to the specific format required by the fine-tuned model Stable Beluga\n",
-    "def mk_prompt(user, syst=\"### System:\\nYou are a useful AI assistant in a car, that follows instructions extremely well. Help as much as you can. Answer questions concisely and do not mention what you base your reply on.\\n\\n\"): return f\"{syst}### User: {user}\\n\\n### Assistant:\\n\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "id": "yAJI0WyOLE8G"
-   },
-   "outputs": [],
-   "source": [
-    "def car_answer_only(complete_answer, general_context):\n",
-    "    \"\"\"returns only the AI assistant answer, without all context, to reply to the user\"\"\"\n",
-    "    pattern = r\"Assistant:\\\\n(.*)(</s>|[.!?](\\s|$))\" #pattern = r\"Assistant:\\\\n(.*?)</s>\"\n",
-    "\n",
-    "    match = re.search(pattern, complete_answer, re.DOTALL)\n",
-    "\n",
-    "    if match:\n",
-    "        # Extracting the text\n",
-    "        model_answer = match.group(1)\n",
-    "        #print(complete_answer)\n",
-    "    else:\n",
-    "        #print(complete_answer)\n",
-    "        model_answer = \"There has been an error with the generated response.\" \n",
-    "\n",
-    "    general_context +=  model_answer\n",
-    "    return (model_answer, general_context)\n",
-    "#print(model_answer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "id": "ViCEgogaENNV"
-   },
-   "outputs": [],
-   "source": [
-    "def FnAnswer(general_context, ques, place, time, delete_history, state):\n",
-    "    \"\"\"function to manage the two different llms (function calling and basic answer) and call them one after the other\"\"\"\n",
-    "    # Initialize state if it is None\n",
-    "    if delete_history == \"Yes\":\n",
-    "        state = None\n",
-    "    if state is None:\n",
-    "        conv_context = []\n",
-    "        conv_context.append(general_context)\n",
-    "        state = {}\n",
-    "        state['context'] = conv_context\n",
-    "        state['number'] = 0\n",
-    "        state['last_question'] = \"\"\n",
-    "        \n",
-    "    if type(ques) != str: \n",
-    "        ques = ques[0]\n",
-    "        \n",
-    "    place = definePlace(place) #which on the predefined places it is\n",
-    "    \n",
-    "    formatted_context = '\\n'.join(state['context'])\n",
-    "        \n",
-    "    #updated at every question\n",
-    "    general_context = f\"\"\"\n",
-    "    Recent conversation history: '{formatted_context}' (If empty, this indicates the beginning of the conversation).\n",
-    "\n",
-    "    Previous question from the user: '{state['last_question']}' (This may or may not be related to the current question).\n",
-    "\n",
-    "    User information: The user is inside a car in {place[0]}, with latitude {place[1]} and longitude {place[2]}. The user is mobile and can drive to different destinations. It is currently {time}\n",
-    "\n",
-    "    \"\"\"\n",
-    "    #first llm call (function calling model, NexusRaven)\n",
-    "    model_output= pipe(construct_prompt(ques, general_context), do_sample=False, max_new_tokens=300, return_full_text=False)\n",
-    "    call = execute_function_call(model_output) #call variable is formatted to as a call to a specific function with the required parameters\n",
-    "    print(call)\n",
-    "    #this is what will erase the model_output from the GPU memory to free up space\n",
-    "    del model_output\n",
-    "    import gc         # garbage collect library\n",
-    "    gc.collect()\n",
-    "    torch.cuda.empty_cache() \n",
-    "        \n",
-    "    #updated at every question\n",
-    "    general_context += f'This information might be of help, use if it seems relevant, and ignore if not relevant to reply to the user: \"{call}\". '\n",
-    "    \n",
-    "    #question formatted for the StableBeluga llm (second llm), using the output of the first llm as context in general_context\n",
-    "    question=f\"\"\"Reply to the user and answer any question with the help of the provided context.\n",
-    "\n",
-    "    ## Context\n",
-    "\n",
-    "    {general_context} .\n",
-    "\n",
-    "    ## Question\n",
-    "\n",
-    "    {ques}\"\"\"\n",
-    "\n",
-    "    complete_answer = str(gen(mk_prompt(question), 100)) #answer generation with StableBeluga (2nd llm)\n",
-    "\n",
-    "    model_answer, general_context= car_answer_only(complete_answer, general_context) #to retrieve only the car answer \n",
-    "    \n",
-    "    language = pipe_language(model_answer, top_k=1, truncation=True)[0]['label'] #detect the language of the answer, to modify the text-to-speech consequently\n",
-    "    \n",
-    "    state['last_question'] = ques #add the current question as 'last question' for the next question's context\n",
-    "    \n",
-    "    state['number']= state['number'] + 1  #adds 1 to the number of interactions with the car\n",
-    "\n",
-    "    state['context'].append(str(state['number']) + '. User question: '+ ques + ', Model answer: ' + model_answer) #modifies the context\n",
-    "    \n",
-    "    #print(\"contexte : \" + '\\n'.join(state['context']))\n",
-    "    \n",
-    "    if len(state['context'])>5: #6 questions maximum in the context to avoid having too many information\n",
-    "        state['context'] = state['context'][1:]\n",
-    "\n",
-    "    return model_answer, state['context'], state, language"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "id": "9WQlYePVLrTN"
-   },
-   "outputs": [],
-   "source": [
-    "def transcript(general_context, link_to_audio, voice, place, time, delete_history, state):\n",
-    "    \"\"\"this function manages speech-to-text to input Fnanswer function and text-to-speech with the Fnanswer output\"\"\"\n",
-    "    # load audio from a specific path\n",
-    "    audio_path = link_to_audio\n",
-    "    audio_array, sampling_rate = librosa.load(link_to_audio, sr=16000)  # \"sr=16000\" ensures that the sampling rate is as required\n",
-    "\n",
-    "\n",
-    "    # process the audio array\n",
-    "    input_features = processor(audio_array, sampling_rate, return_tensors=\"pt\").input_features\n",
-    "\n",
-    "\n",
-    "    predicted_ids = modelw.generate(input_features)\n",
-    "\n",
-    "    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)\n",
-    "\n",
-    "    quest_processing = FnAnswer(general_context, transcription, place, time, delete_history, state)\n",
-    "    \n",
-    "    state=quest_processing[2]\n",
-    "    \n",
-    "    print(\"langue \" + quest_processing[3])\n",
-    "\n",
-    "    tts.tts_to_file(text= str(quest_processing[0]),\n",
-    "                file_path=\"output.wav\",\n",
-    "                speaker_wav=f'Audio_Files/{voice}.wav',\n",
-    "                language=quest_processing[3],\n",
-    "                emotion = \"angry\")\n",
-    "\n",
-    "    audio_path = \"output.wav\"\n",
-    "    return audio_path, state['context'], state"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def definePlace(place):\n",
-    "    if(place == 'Luxembourg Gare, Luxembourg'):\n",
-    "        return('Luxembourg Gare', '49.5999681', '6.1342493' )\n",
-    "    elif (place =='Kirchberg Campus, Kirchberg'):\n",
-    "        return('Kirchberg Campus, Luxembourg', '49.62571206478235', '6.160082636815114')\n",
-    "    elif (place =='Belval Campus, Belval'):\n",
-    "        return('Belval-Université, Esch-sur-Alzette', '49.499531', '5.9462903')\n",
-    "    elif (place =='Eiffel Tower, Paris'):\n",
-    "        return('Eiffel Tower, Paris', '48.8582599', '2.2945006')\n",
-    "    elif (place=='Thionville, France'):\n",
-    "        return('Thionville, France', '49.357927', '6.167587')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Interfaces (text and audio)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Running on local URL:  http://0.0.0.0:7860\n",
-      "\n",
-      "Could not create share link. Please check your internet connection or our status page: https://status.gradio.app.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024/03/15 19:02:04 [W] [service.go:132] login to server failed: dial tcp 44.237.78.176:7000: i/o timeout\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://localhost:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Keyboard interruption in main thread... closing server.\n",
-      "Killing tunnel 0.0.0.0:7860 <> None\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#INTERFACE WITH ONLY TEXT\n",
-    "\n",
-    "# Generate options for hours (00-23) \n",
-    "hour_options = [f\"{i:02d}:00:00\" for i in range(24)]\n",
-    "\n",
-    "model_answer= ''\n",
-    "general_context= ''\n",
-    "# Define the initial state with some initial context.\n",
-    "print(general_context)\n",
-    "initial_state = {'context': general_context}\n",
-    "initial_context= initial_state['context']\n",
-    "# Create the Gradio interface.\n",
-    "iface = gr.Interface(\n",
-    "    fn=FnAnswer,\n",
-    "    inputs=[\n",
-    "        gr.Textbox(value=initial_context, visible=False),\n",
-    "        gr.Textbox(lines=2, placeholder=\"Type your message here...\"),\n",
-    "        gr.Radio(choices=['Luxembourg Gare, Luxembourg', 'Kirchberg Campus, Kirchberg', 'Belval Campus, Belval', 'Eiffel Tower, Paris', 'Thionville, France'], label='Choose a location for your car', value= 'Kirchberg Campus, Kirchberg', show_label=True),\n",
-    "        gr.Dropdown(choices=hour_options, label=\"What time is it?\", value = \"08:00:00\"),\n",
-    "        gr.Radio([\"Yes\", \"No\"], label=\"Delete the conversation history?\", value = 'No'),\n",
-    "        gr.State()  # This will keep track of the context state across interactions.\n",
-    "    ],\n",
-    "    outputs=[\n",
-    "        gr.Textbox(),\n",
-    "        gr.Textbox(visible=False),\n",
-    "        gr.State()\n",
-    "    ]\n",
-    ")\n",
-    "gr.close_all()\n",
-    "# Launch the interface.\n",
-    "iface.launch(debug=True, share=True, server_name=\"0.0.0.0\", server_port=7860)\n",
-    "#contextual=gr.Textbox(value=general_context, visible=False)\n",
-    "#demo = gr.Interface(fn=FnAnswer, inputs=[contextual,\"text\"], outputs=[\"text\", contextual])\n",
-    "\n",
-    "#demo.launch()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "collapsed": true,
-    "id": "mZTt3y3_KOOF"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Closing server running on port: 7860\n",
-      "Closing server running on port: 7860\n",
-      "Closing server running on port: 7860\n",
-      "Closing server running on port: 7860\n",
-      "Closing server running on port: 7860\n",
-      "Closing server running on port: 7860\n",
-      "Closing server running on port: 7860\n",
-      "Closing server running on port: 7860\n",
-      "Closing server running on port: 7860\n",
-      "Closing server running on port: 7860\n",
-      "Running on local URL:  http://0.0.0.0:7860\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[69], line 42\u001b[0m\n\u001b[1;32m     40\u001b[0m gr\u001b[38;5;241m.\u001b[39mclose_all()\n\u001b[1;32m     41\u001b[0m \u001b[38;5;66;03m# Launch the interface.\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m \u001b[43miface\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlaunch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshare\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mserver_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m0.0.0.0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mserver_port\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m7860\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mssl_verify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/gradio/blocks.py:2068\u001b[0m, in \u001b[0;36mBlocks.launch\u001b[0;34m(self, inline, inbrowser, share, debug, max_threads, auth, auth_message, prevent_thread_lock, show_error, server_name, server_port, height, width, favicon_path, ssl_keyfile, ssl_certfile, ssl_keyfile_password, ssl_verify, quiet, show_api, allowed_paths, blocked_paths, root_path, app_kwargs, state_session_capacity, share_server_address, share_server_protocol, _frontend)\u001b[0m\n\u001b[1;32m   2066\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2067\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshare_url \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2068\u001b[0m         share_url \u001b[38;5;241m=\u001b[39m \u001b[43mnetworking\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetup_tunnel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2069\u001b[0m \u001b[43m            \u001b[49m\u001b[43mlocal_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mserver_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2070\u001b[0m \u001b[43m            \u001b[49m\u001b[43mlocal_port\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mserver_port\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2071\u001b[0m \u001b[43m            \u001b[49m\u001b[43mshare_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshare_token\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2072\u001b[0m \u001b[43m            \u001b[49m\u001b[43mshare_server_address\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshare_server_address\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2073\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2074\u001b[0m         parsed_url \u001b[38;5;241m=\u001b[39m urlparse(share_url)\n\u001b[1;32m   2075\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshare_url \u001b[38;5;241m=\u001b[39m urlunparse(\n\u001b[1;32m   2076\u001b[0m             (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshare_server_protocol,) \u001b[38;5;241m+\u001b[39m parsed_url[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m   2077\u001b[0m         )\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/gradio/networking.py:229\u001b[0m, in \u001b[0;36msetup_tunnel\u001b[0;34m(local_host, local_port, share_token, share_server_address)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m share_server_address \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 229\u001b[0m         response \u001b[38;5;241m=\u001b[39m \u001b[43mhttpx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mGRADIO_API_SERVER\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    230\u001b[0m         payload \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mjson()[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    231\u001b[0m         remote_host, remote_port \u001b[38;5;241m=\u001b[39m payload[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhost\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;28mint\u001b[39m(payload[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mport\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_api.py:196\u001b[0m, in \u001b[0;36mget\u001b[0;34m(url, params, headers, cookies, auth, proxy, proxies, follow_redirects, cert, verify, timeout, trust_env)\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(\n\u001b[1;32m    174\u001b[0m     url: URLTypes,\n\u001b[1;32m    175\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    186\u001b[0m     trust_env: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    187\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Response:\n\u001b[1;32m    188\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    189\u001b[0m \u001b[38;5;124;03m    Sends a `GET` request.\u001b[39;00m\n\u001b[1;32m    190\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[38;5;124;03m    on this function, as `GET` requests should not include a request body.\u001b[39;00m\n\u001b[1;32m    195\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 196\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    197\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    198\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    199\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    200\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    201\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcookies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcookies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    202\u001b[0m \u001b[43m        \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    203\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproxy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    204\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproxies\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mproxies\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    205\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    206\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcert\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    207\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    208\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    209\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtrust_env\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrust_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    210\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_api.py:104\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m(method, url, params, content, data, files, json, headers, cookies, auth, proxy, proxies, timeout, follow_redirects, verify, cert, trust_env)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;124;03mSends an HTTP request.\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[38;5;124;03m```\u001b[39;00m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Client(\n\u001b[1;32m     96\u001b[0m     cookies\u001b[38;5;241m=\u001b[39mcookies,\n\u001b[1;32m     97\u001b[0m     proxy\u001b[38;5;241m=\u001b[39mproxy,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    102\u001b[0m     trust_env\u001b[38;5;241m=\u001b[39mtrust_env,\n\u001b[1;32m    103\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m client:\n\u001b[0;32m--> 104\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    105\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    106\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    107\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcontent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontent\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    108\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    109\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    110\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    111\u001b[0m \u001b[43m        \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    112\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    113\u001b[0m \u001b[43m        \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    114\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    115\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py:828\u001b[0m, in \u001b[0;36mClient.request\u001b[0;34m(self, method, url, content, data, files, json, params, headers, cookies, auth, follow_redirects, timeout, extensions)\u001b[0m\n\u001b[1;32m    813\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(message, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n\u001b[1;32m    815\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_request(\n\u001b[1;32m    816\u001b[0m     method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[1;32m    817\u001b[0m     url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    826\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mextensions,\n\u001b[1;32m    827\u001b[0m )\n\u001b[0;32m--> 828\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py:915\u001b[0m, in \u001b[0;36mClient.send\u001b[0;34m(self, request, stream, auth, follow_redirects)\u001b[0m\n\u001b[1;32m    907\u001b[0m follow_redirects \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    908\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfollow_redirects\n\u001b[1;32m    909\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(follow_redirects, UseClientDefault)\n\u001b[1;32m    910\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m follow_redirects\n\u001b[1;32m    911\u001b[0m )\n\u001b[1;32m    913\u001b[0m auth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_request_auth(request, auth)\n\u001b[0;32m--> 915\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    916\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    917\u001b[0m \u001b[43m    \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    918\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    919\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    920\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    921\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    922\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py:943\u001b[0m, in \u001b[0;36mClient._send_handling_auth\u001b[0;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[1;32m    940\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[1;32m    942\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 943\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    944\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    945\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    946\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    947\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    948\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    949\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py:980\u001b[0m, in \u001b[0;36mClient._send_handling_redirects\u001b[0;34m(self, request, follow_redirects, history)\u001b[0m\n\u001b[1;32m    977\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequest\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    978\u001b[0m     hook(request)\n\u001b[0;32m--> 980\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    981\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    982\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_client.py:1016\u001b[0m, in \u001b[0;36mClient._send_single_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m   1011\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m   1012\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1013\u001b[0m     )\n\u001b[1;32m   1015\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request\u001b[38;5;241m=\u001b[39mrequest):\n\u001b[0;32m-> 1016\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mtransport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1018\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response\u001b[38;5;241m.\u001b[39mstream, SyncByteStream)\n\u001b[1;32m   1020\u001b[0m response\u001b[38;5;241m.\u001b[39mrequest \u001b[38;5;241m=\u001b[39m request\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpx/_transports/default.py:231\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    218\u001b[0m req \u001b[38;5;241m=\u001b[39m httpcore\u001b[38;5;241m.\u001b[39mRequest(\n\u001b[1;32m    219\u001b[0m     method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[1;32m    220\u001b[0m     url\u001b[38;5;241m=\u001b[39mhttpcore\u001b[38;5;241m.\u001b[39mURL(\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    228\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[1;32m    229\u001b[0m )\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[0;32m--> 231\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    233\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m    235\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[1;32m    236\u001b[0m     status_code\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mstatus,\n\u001b[1;32m    237\u001b[0m     headers\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[1;32m    238\u001b[0m     stream\u001b[38;5;241m=\u001b[39mResponseStream(resp\u001b[38;5;241m.\u001b[39mstream),\n\u001b[1;32m    239\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[1;32m    240\u001b[0m )\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection_pool.py:268\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    266\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ShieldCancellation():\n\u001b[1;32m    267\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresponse_closed(status)\n\u001b[0;32m--> 268\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m    269\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection_pool.py:251\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m    250\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 251\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[1;32m    253\u001b[0m     \u001b[38;5;66;03m# The ConnectionNotAvailable exception is a special case, that\u001b[39;00m\n\u001b[1;32m    254\u001b[0m     \u001b[38;5;66;03m# indicates we need to retry the request on a new connection.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[38;5;66;03m# might end up as an HTTP/2 connection, but which actually ends\u001b[39;00m\n\u001b[1;32m    259\u001b[0m     \u001b[38;5;66;03m# up as HTTP/1.1.\u001b[39;00m\n\u001b[1;32m    260\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pool_lock:\n\u001b[1;32m    261\u001b[0m         \u001b[38;5;66;03m# Maintain our position in the request queue, but reset the\u001b[39;00m\n\u001b[1;32m    262\u001b[0m         \u001b[38;5;66;03m# status so that the request becomes queued again.\u001b[39;00m\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/connection.py:103\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39mis_available():\n\u001b[1;32m    101\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectionNotAvailable()\n\u001b[0;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py:133\u001b[0m, in \u001b[0;36mHTTP11Connection.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse_closed\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[1;32m    132\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_response_closed()\n\u001b[0;32m--> 133\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py:111\u001b[0m, in \u001b[0;36mHTTP11Connection.handle_request\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    101\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\n\u001b[1;32m    104\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreceive_response_headers\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request, kwargs\n\u001b[1;32m    105\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[1;32m    106\u001b[0m     (\n\u001b[1;32m    107\u001b[0m         http_version,\n\u001b[1;32m    108\u001b[0m         status,\n\u001b[1;32m    109\u001b[0m         reason_phrase,\n\u001b[1;32m    110\u001b[0m         headers,\n\u001b[0;32m--> 111\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_receive_response_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    112\u001b[0m     trace\u001b[38;5;241m.\u001b[39mreturn_value \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    113\u001b[0m         http_version,\n\u001b[1;32m    114\u001b[0m         status,\n\u001b[1;32m    115\u001b[0m         reason_phrase,\n\u001b[1;32m    116\u001b[0m         headers,\n\u001b[1;32m    117\u001b[0m     )\n\u001b[1;32m    119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[1;32m    120\u001b[0m     status\u001b[38;5;241m=\u001b[39mstatus,\n\u001b[1;32m    121\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    127\u001b[0m     },\n\u001b[1;32m    128\u001b[0m )\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py:176\u001b[0m, in \u001b[0;36mHTTP11Connection._receive_response_headers\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m    173\u001b[0m timeout \u001b[38;5;241m=\u001b[39m timeouts\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mread\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    175\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 176\u001b[0m     event \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_receive_event\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    177\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(event, h11\u001b[38;5;241m.\u001b[39mResponse):\n\u001b[1;32m    178\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_sync/http11.py:212\u001b[0m, in \u001b[0;36mHTTP11Connection._receive_event\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    209\u001b[0m     event \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h11_state\u001b[38;5;241m.\u001b[39mnext_event()\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event \u001b[38;5;129;01mis\u001b[39;00m h11\u001b[38;5;241m.\u001b[39mNEED_DATA:\n\u001b[0;32m--> 212\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_network_stream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    213\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mREAD_NUM_BYTES\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\n\u001b[1;32m    214\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# If we feed this case through h11 we'll raise an exception like:\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;66;03m#     httpcore.RemoteProtocolError: can't handle event type\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[38;5;66;03m# perspective. Instead we handle this case distinctly and treat\u001b[39;00m\n\u001b[1;32m    223\u001b[0m     \u001b[38;5;66;03m# it as a ConnectError.\u001b[39;00m\n\u001b[1;32m    224\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;241m==\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h11_state\u001b[38;5;241m.\u001b[39mtheir_state \u001b[38;5;241m==\u001b[39m h11\u001b[38;5;241m.\u001b[39mSEND_RESPONSE:\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/httpcore/_backends/sync.py:126\u001b[0m, in \u001b[0;36mSyncStream.read\u001b[0;34m(self, max_bytes, timeout)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39msettimeout(timeout)\n\u001b[0;32m--> 126\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_bytes\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m/usr/lib/python3.10/ssl.py:1259\u001b[0m, in \u001b[0;36mSSLSocket.recv\u001b[0;34m(self, buflen, flags)\u001b[0m\n\u001b[1;32m   1255\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1256\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1257\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1258\u001b[0m             \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1259\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuflen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1260\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1261\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv(buflen, flags)\n",
-      "File \u001b[0;32m/usr/lib/python3.10/ssl.py:1132\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1130\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[1;32m   1131\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1132\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1133\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SSLError \u001b[38;5;28;01mas\u001b[39;00m x:\n\u001b[1;32m   1134\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39margs[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m SSL_ERROR_EOF \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msuppress_ragged_eofs:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "#INTERFACE WITH AUDIO TO AUDIO\n",
-    "\n",
-    "#to be able to use the microphone on chrome, you will have to go to chrome://flags/#unsafely-treat-insecure-origin-as-secure and enter http://10.186.115.21:7860/ \n",
-    "#in \"Insecure origins treated as secure\", enable it and relaunch chrome\n",
-    "\n",
-    "#example question: \n",
-    "# what's the weather like outside?\n",
-    "# What's the closest restaurant from here?\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Generate options for hours (00-23) \n",
-    "hour_options = [f\"{i:02d}:00:00\" for i in range(24)]\n",
-    "\n",
-    "model_answer= ''\n",
-    "general_context= ''\n",
-    "# Define the initial state with some initial context.\n",
-    "print(general_context)\n",
-    "initial_state = {'context': general_context}\n",
-    "initial_context= initial_state['context']\n",
-    "# Create the Gradio interface.\n",
-    "iface = gr.Interface(\n",
-    "    fn=transcript,\n",
-    "    inputs=[\n",
-    "        gr.Textbox(value=initial_context, visible=False),\n",
-    "        gr.Audio( type='filepath', label = 'input audio'),\n",
-    "        gr.Radio(choices=['Donald Trump', 'Eddie Murphy'], label='Choose a voice', value= 'Donald Trump', show_label=True),  # Radio button for voice selection\n",
-    "        gr.Radio(choices=['Luxembourg Gare, Luxembourg', 'Kirchberg Campus, Kirchberg', 'Belval Campus, Belval', 'Eiffel Tower, Paris', 'Thionville, France'], label='Choose a location for your car', value= 'Kirchberg Campus, Kirchberg', show_label=True),\n",
-    "        gr.Dropdown(choices=hour_options, label=\"What time is it?\", value = \"08:00:00\"),\n",
-    "        gr.Radio([\"Yes\", \"No\"], label=\"Delete the conversation history?\", value = 'No'),\n",
-    "        gr.State()  # This will keep track of the context state across interactions.\n",
-    "    ],\n",
-    "    outputs=[\n",
-    "        gr.Audio(label = 'output audio'),\n",
-    "        gr.Textbox(visible=False),\n",
-    "        gr.State()\n",
-    "    ]\n",
-    ")\n",
-    "#close all interfaces open to make the port available\n",
-    "gr.close_all()\n",
-    "# Launch the interface.\n",
-    "iface.launch(debug=True, share=True, server_name=\"0.0.0.0\", server_port=7860, ssl_verify=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Other possible APIs to use"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "JSONDecodeError",
-     "evalue": "Expecting value: line 1 column 1 (char 0)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py:971\u001b[0m, in \u001b[0;36mResponse.json\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    970\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 971\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcomplexjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    972\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m JSONDecodeError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;66;03m# Catch JSON-related errors and raise as requests.JSONDecodeError\u001b[39;00m\n\u001b[1;32m    974\u001b[0m     \u001b[38;5;66;03m# This aliases json.JSONDecodeError and simplejson.JSONDecodeError\u001b[39;00m\n",
-      "File \u001b[0;32m/usr/lib/python3.10/json/__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    344\u001b[0m         parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    345\u001b[0m         parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[0;32m--> 346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_decoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m/usr/lib/python3.10/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m    334\u001b[0m \u001b[38;5;124;03mcontaining a JSON document).\u001b[39;00m\n\u001b[1;32m    335\u001b[0m \n\u001b[1;32m    336\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_w\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    338\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n",
-      "File \u001b[0;32m/usr/lib/python3.10/json/decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 355\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpecting value\u001b[39m\u001b[38;5;124m\"\u001b[39m, s, err\u001b[38;5;241m.\u001b[39mvalue) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj, end\n",
-      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[80], line 25\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m     22\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(results)\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43msearch_nearby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m49.625892805337514\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m6.160417066963513\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43myour location\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mTOMTOM_KEY\u001b[49m\u001b[43m)\u001b[49m)\n",
-      "Cell \u001b[0;32mIn[80], line 17\u001b[0m, in \u001b[0;36msearch_nearby\u001b[0;34m(lat, lon, city, key)\u001b[0m\n\u001b[1;32m      9\u001b[0m results \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     11\u001b[0m r \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://api.tomtom.com/search/2/nearbySearch/.json?key=\u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;124m&lat=\u001b[39m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;124m&lon=\u001b[39m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m&radius=10000&limit=50\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m     12\u001b[0m                     key,\n\u001b[1;32m     13\u001b[0m                     lat,\n\u001b[1;32m     14\u001b[0m                     lon\n\u001b[1;32m     15\u001b[0m ))\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m \u001b[43mr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresults\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[1;32m     18\u001b[0m     results\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoi\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcategories\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresult[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpoi\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mint\u001b[39m(result[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdist\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m meters far from \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcity\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(results) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m7\u001b[39m:\n",
-      "File \u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py:975\u001b[0m, in \u001b[0;36mResponse.json\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    971\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m complexjson\u001b[38;5;241m.\u001b[39mloads(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtext, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    972\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m JSONDecodeError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;66;03m# Catch JSON-related errors and raise as requests.JSONDecodeError\u001b[39;00m\n\u001b[1;32m    974\u001b[0m     \u001b[38;5;66;03m# This aliases json.JSONDecodeError and simplejson.JSONDecodeError\u001b[39;00m\n\u001b[0;32m--> 975\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m RequestsJSONDecodeError(e\u001b[38;5;241m.\u001b[39mmsg, e\u001b[38;5;241m.\u001b[39mdoc, e\u001b[38;5;241m.\u001b[39mpos)\n",
-      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "def search_nearby(lat, lon, city, key):\n",
-    "    \"\"\"\n",
-    "    :param lat: latitude\n",
-    "    :param lon: longitude\n",
-    "    :param key: api key\n",
-    "    :param type: type of poi\n",
-    "    :return: [5] results ['poi']['name']/['freeformAddress'] || ['position']['lat']/['lon']\n",
-    "    \"\"\"\n",
-    "    results = []\n",
-    "\n",
-    "    r = requests.get('https://api.tomtom.com/search/2/nearbySearch/.json?key={0}&lat={1}&lon={2}&radius=10000&limit=50'.format(\n",
-    "                        key,\n",
-    "                        lat,\n",
-    "                        lon\n",
-    "    ))\n",
-    "\n",
-    "    for result in r.json()['results']:\n",
-    "        results.append(f\"The {' '.join(result['poi']['categories'])} {result['poi']['name']} is {int(result['dist'])} meters far from {city}\")\n",
-    "        if len(results) == 7:\n",
-    "            break\n",
-    "\n",
-    "    return \". \".join(results)\n",
-    "\n",
-    "\n",
-    "print(search_nearby('49.625892805337514', '6.160417066963513', 'your location', TOMTOM_KEY))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "gpuType": "T4",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}