import gradio as gr import requests import os import time import re import logging import tempfile import folium import concurrent.futures import torch from PIL import Image from datetime import datetime from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor from googlemaps import Client as GoogleMapsClient from gtts import gTTS from diffusers import StableDiffusionPipeline from langchain_openai import OpenAIEmbeddings, ChatOpenAI from langchain_pinecone import PineconeVectorStore from langchain.prompts import PromptTemplate from langchain.chains import RetrievalQA from langchain.chains.conversation.memory import ConversationBufferWindowMemory from huggingface_hub import login from transformers.models.speecht5.number_normalizer import EnglishNumberNormalizer from parler_tts import ParlerTTSForConditionalGeneration from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed from scipy.io.wavfile import write as write_wav from pydub import AudioSegment from string import punctuation import librosa from pathlib import Path import torchaudio import numpy as np # Neo4j imports from langchain.chains import GraphCypherQAChain from langchain_community.graphs import Neo4jGraph from langchain_community.document_loaders import HuggingFaceDatasetLoader from langchain_text_splitters import CharacterTextSplitter from langchain_experimental.graph_transformers import LLMGraphTransformer from langchain_core.prompts import ChatPromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.messages import AIMessage, HumanMessage from langchain_core.output_parsers import StrOutputParser from langchain_core.runnables import RunnableBranch, RunnableLambda, RunnableParallel, RunnablePassthrough from serpapi.google_search import GoogleSearch # Set environment variables for CUDA os.environ['PYTORCH_USE_CUDA_DSA'] = '1' os.environ['CUDA_LAUNCH_BLOCKING'] = '1' hf_token = os.getenv("HF_TOKEN") if hf_token is None: print("Please set your Hugging Face token in the environment variables.") else: login(token=hf_token) logging.basicConfig(level=logging.DEBUG) embeddings = OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY']) # Pinecone setup from pinecone import Pinecone pc = Pinecone(api_key=os.environ['PINECONE_API_KEY']) index_name = "radardata07242024" vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings) retriever = vectorstore.as_retriever(search_kwargs={'k': 5}) chat_model = ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0, model='gpt-4o') conversational_memory = ConversationBufferWindowMemory( memory_key='chat_history', k=10, return_messages=True ) # Prompt templates def get_current_date(): return datetime.now().strftime("%B %d, %Y") current_date = get_current_date() template1 = f"""As an expert concierge in Birmingham, Alabama, known for being a helpful and renowned guide, I am here to assist you on this sunny bright day of {current_date}. Given the current weather conditions and date, I have access to a plethora of information regarding events, places, and activities in Birmingham that can enhance your experience. If you have any questions or need recommendations, feel free to ask. I have a wealth of knowledge of perennial events in Birmingham and can provide detailed information to ensure you make the most of your time here. Remember, I am here to assist you in any way possible. Now, let me guide you through some of the exciting events happening today in Birmingham, Alabama: Address: >>, Birmingham, AL Time: >>__ Date: >>__ Description: >>__ Address: >>, Birmingham, AL Time: >>__ Date: >>__ Description: >>__ Address: >>, Birmingham, AL Time: >>__ Date: >>__ Description: >>__ Address: >>, Birmingham, AL Time: >>__ Date: >>__ Description: >>__ Address: >>, Birmingham, AL Time: >>__ Date: >>__ Description: >>__ If you have any specific preferences or questions about these events or any other inquiries, please feel free to ask. Remember, I am here to ensure you have a memorable and enjoyable experience in Birmingham, AL. It was my pleasure! {{context}} Question: {{question}} Helpful Answer:""" template2 = f"""As an expert concierge known for being helpful and a renowned guide for Birmingham, Alabama, I assist visitors in discovering the best that the city has to offer. Given today's sunny and bright weather on {current_date}, I am well-equipped to provide valuable insights and recommendations without revealing specific locations. I draw upon my extensive knowledge of the area, including perennial events and historical context. In light of this, how can I assist you today? Feel free to ask any questions or seek recommendations for your day in Birmingham. If there's anything specific you'd like to know or experience, please share, and I'll be glad to help. Remember, keep the question concise for a quick and accurate response. "It was my pleasure!" {{context}} Question: {{question}} Helpful Answer:""" QA_CHAIN_PROMPT_1 = PromptTemplate(input_variables=["context", "question"], template=template1) QA_CHAIN_PROMPT_2 = PromptTemplate(input_variables=["context", "question"], template=template2) # Neo4j setup graph = Neo4jGraph( url="neo4j+s://98f45cc0.databases.neo4j.io", username="neo4j", password="B_sZbapCTZoQDWj1JrhwqElsNa-jm5Zq1m_mAnyPYog" ) # Avoid pushing the graph documents to Neo4j every time # Only push the documents once and comment the code below after the initial push # dataset_name = "Pijush2023/birmindata07312024" # page_content_column = 'events_description' # loader = HuggingFaceDatasetLoader(dataset_name, page_content_column) # data = loader.load() # text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=50) # documents = text_splitter.split_documents(data) # llm_transformer = LLMGraphTransformer(llm=chat_model) # graph_documents = llm_transformer.convert_to_graph_documents(documents) # graph.add_graph_documents(graph_documents, baseEntityLabel=True, include_source=True) class Entities(BaseModel): names: list[str] = Field(..., description="All the person, organization, or business entities that appear in the text") entity_prompt = ChatPromptTemplate.from_messages([ ("system", "You are extracting organization and person entities from the text."), ("human", "Use the given format to extract information from the following input: {question}"), ]) entity_chain = entity_prompt | chat_model.with_structured_output(Entities) def remove_lucene_chars(input: str) -> str: return input.translate(str.maketrans({"\\": r"\\", "+": r"\+", "-": r"\-", "&": r"\&", "|": r"\|", "!": r"\!", "(": r"\(", ")": r"\)", "{": r"\{", "}": r"\}", "[": r"\[", "]": r"\]", "^": r"\^", "~": r"\~", "*": r"\*", "?": r"\?", ":": r"\:", '"': r'\"', ";": r"\;", " ": r"\ "})) def generate_full_text_query(input: str) -> str: full_text_query = "" words = [el for el in remove_lucene_chars(input).split() if el] for word in words[:-1]: full_text_query += f" {word}~2 AND" full_text_query += f" {words[-1]}~2" return full_text_query.strip() def structured_retriever(question: str) -> str: result = "" entities = entity_chain.invoke({"question": question}) for entity in entities.names: response = graph.query( """CALL db.index.fulltext.queryNodes('entity', $query, {limit:2}) YIELD node,score CALL { WITH node MATCH (node)-[r:!MENTIONS]->(neighbor) RETURN node.id + ' - ' + type(r) + ' -> ' + neighbor.id AS output UNION ALL WITH node MATCH (node)<-[r:!MENTIONS]-(neighbor) RETURN neighbor.id + ' - ' + type(r) + ' -> ' + node.id AS output } RETURN output LIMIT 50 """, {"query": generate_full_text_query(entity)}, ) result += "\n".join([el['output'] for el in response]) return result def retriever_neo4j(question: str): structured_data = structured_retriever(question) logging.debug(f"Structured data: {structured_data}") return structured_data _template = """Given the following conversation and a follow-up question, rephrase the follow-up question to be a standalone question, in its original language. Chat History: {chat_history} Follow Up Input: {question} Standalone question:""" CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template) def _format_chat_history(chat_history: list[tuple[str, str]]) -> list: buffer = [] for human, ai in chat_history: buffer.append(HumanMessage(content=human)) buffer.append(AIMessage(content=ai)) return buffer _search_query = RunnableBranch( ( RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config( run_name="HasChatHistoryCheck" ), RunnablePassthrough.assign( chat_history=lambda x: _format_chat_history(x["chat_history"]) ) | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0, api_key=os.environ['OPENAI_API_KEY']) | StrOutputParser(), ), RunnableLambda(lambda x : x["question"]), ) # template = """Answer the question based only on the following context: # {context} # Question: {question} # Use natural language and be concise. # Answer:""" template = f"""As an expert concierge known for being helpful and a renowned guide for Birmingham, Alabama, I assist visitors in discovering the best that the city has to offer. Given today's sunny and bright weather on {current_date}, I am well-equipped to provide valuable insights and recommendations without revealing specific locations. I draw upon my extensive knowledge of the area, including perennial events and historical context. In light of this, how can I assist you today? Feel free to ask any questions or seek recommendations for your day in Birmingham. If there's anything specific you'd like to know or experience, please share, and I'll be glad to help. Remember, keep the question concise for a quick,short ,crisp and accurate response. "It was my pleasure!" {{context}} Question: {{question}} Helpful Answer:""" qa_prompt = ChatPromptTemplate.from_template(template) chain_neo4j = ( RunnableParallel( { "context": _search_query | retriever_neo4j, "question": RunnablePassthrough(), } ) | qa_prompt | chat_model | StrOutputParser() ) # Define a function to select between Pinecone and Neo4j # def generate_answer(message, choice, retrieval_mode): # logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}") # prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2 # if retrieval_mode == "VDB": # qa_chain = RetrievalQA.from_chain_type( # llm=chat_model, # chain_type="stuff", # retriever=retriever, # chain_type_kwargs={"prompt": prompt_template} # ) # response = qa_chain({"query": message}) # logging.debug(f"Vector response: {response}") # return response['result'], extract_addresses(response['result']) # elif retrieval_mode == "KGF": # response = chain_neo4j.invoke({"question": message}) # logging.debug(f"Knowledge-Graph response: {response}") # return response, extract_addresses(response) # else: # return "Invalid retrieval mode selected.", [] # def bot(history, choice, tts_choice, retrieval_mode): # if not history: # return history # response, addresses = generate_answer(history[-1][0], choice, retrieval_mode) # history[-1][1] = "" # with concurrent.futures.ThreadPoolExecutor() as executor: # if tts_choice == "Alpha": # audio_future = executor.submit(generate_audio_elevenlabs, response) # elif tts_choice == "Beta": # audio_future = executor.submit(generate_audio_parler_tts, response) # elif tts_choice == "Gamma": # audio_future = executor.submit(generate_audio_mars5, response) # for character in response: # history[-1][1] += character # time.sleep(0.05) # yield history, None # audio_path = audio_future.result() # yield history, audio_path # history.append([response, None]) # Ensure the response is added in the correct format # def generate_answer(message, choice, retrieval_mode): # logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}") # # Check if the question is about hotels # if "hotel" in message.lower() and "birmingham" in message.lower(): # response = fetch_google_hotels() # return response, extract_addresses(response) # # Check if the question is about restaurants # if "restaurant" in message.lower() and "birmingham" in message.lower(): # response = fetch_yelp_restaurants() # return response, extract_addresses(response) # # Check if the question is about flights # if "flight" in message.lower() and ("JFK" in message or "BHM" in message): # response = fetch_google_flights() # return response, extract_addresses(response) # prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2 # if retrieval_mode == "VDB": # qa_chain = RetrievalQA.from_chain_type( # llm=chat_model, # chain_type="stuff", # retriever=retriever, # chain_type_kwargs={"prompt": prompt_template} # ) # response = qa_chain({"query": message}) # logging.debug(f"Vector response: {response}") # return response['result'], extract_addresses(response['result']) # elif retrieval_mode == "KGF": # response = chain_neo4j.invoke({"question": message}) # logging.debug(f"Knowledge-Graph response: {response}") # return response, extract_addresses(response) # else: # return "Invalid retrieval mode selected.", [] def generate_answer(message, choice, retrieval_mode): logging.debug(f"generate_answer called with choice: {choice} and retrieval_mode: {retrieval_mode}") intro = "" response = "" if "hotel" in message.lower() and "birmingham" in message.lower(): intro = "Here are the top Hotels in Birmingham:" response = fetch_google_hotels() elif "restaurant" in message.lower() and "birmingham" in message.lower(): intro = "Here are the top Restaurants in Birmingham:" response = fetch_yelp_restaurants() elif "flight" in message.lower() and "birmingham" in message.lower(): # intro = "Here are some available flights for today:" response = fetch_google_flights() else: prompt_template = QA_CHAIN_PROMPT_1 if choice == "Details" else QA_CHAIN_PROMPT_2 if retrieval_mode == "VDB": qa_chain = RetrievalQA.from_chain_type( llm=chat_model, chain_type="stuff", retriever=retriever, chain_type_kwargs={"prompt": prompt_template} ) response = qa_chain({"query": message}) logging.debug(f"Vector response: {response}") response = response['result'] elif retrieval_mode == "KGF": response = chain_neo4j.invoke({"question": message}) logging.debug(f"Knowledge-Graph response: {response}") else: response = "Invalid retrieval mode selected." # Assign numbers to each item in the response and format it response_lines = response.splitlines() formatted_response = "" for i, line in enumerate(response_lines): if i == 0: formatted_response += f"{i+1}. {line.strip()}\n" else: formatted_response += f" {line.strip()}\n" # Combine intro and formatted response final_response = f"{intro}\n\n{formatted_response}" return final_response, extract_addresses(response) # def bot(history, choice, tts_choice, retrieval_mode): # if not history: # return history # response, addresses = generate_answer(history[-1][0], choice, retrieval_mode) # history[-1][1] = "" # # Detect if the response is from Yelp (i.e., HTML formatted response) # if "" in response: # for chunk in response.splitlines(): # history[-1][1] += chunk + "\n" # time.sleep(0.1) # Adjust the delay as needed # yield history, None # return # with concurrent.futures.ThreadPoolExecutor() as executor: # if tts_choice == "Alpha": # audio_future = executor.submit(generate_audio_elevenlabs, response) # elif tts_choice == "Beta": # audio_future = executor.submit(generate_audio_parler_tts, response) # elif tts_choice == "Gamma": # audio_future = executor.submit(generate_audio_mars5, response) # for character in response: # history[-1][1] += character # time.sleep(0.05) # yield history, None # audio_path = audio_future.result() # yield history, audio_path # history.append([response, None]) # Ensure the response is added in the correct format def bot(history, choice, tts_choice, retrieval_mode): if not history: return history response, addresses = generate_answer(history[-1][0], choice, retrieval_mode) response = f"
{response}
" # Adjust the font size history[-1][1] = "" with concurrent.futures.ThreadPoolExecutor() as executor: if tts_choice == "Alpha": audio_future = executor.submit(generate_audio_elevenlabs, response) elif tts_choice == "Beta": audio_future = executor.submit(generate_audio_parler_tts, response) elif tts_choice == "Gamma": audio_future = executor.submit(generate_audio_mars5, response) for character in response: history[-1][1] += character time.sleep(0.05) yield history, None audio_path = audio_future.result() yield history, audio_path history.append([response, None]) # Ensure the response is added in the correct format def add_message(history, message): history.append((message, None)) return history, gr.Textbox(value="", interactive=True, placeholder="Enter message or upload file...", show_label=False) def print_like_dislike(x: gr.LikeData): print(x.index, x.value, x.liked) def extract_addresses(response): if not isinstance(response, str): response = str(response) address_patterns = [ r'([A-Z].*,\sBirmingham,\sAL\s\d{5})', r'(\d{4}\s.*,\sBirmingham,\sAL\s\d{5})', r'([A-Z].*,\sAL\s\d{5})', r'([A-Z].*,.*\sSt,\sBirmingham,\sAL\s\d{5})', r'([A-Z].*,.*\sStreets,\sBirmingham,\sAL\s\d{5})', r'(\d{2}.*\sStreets)', r'([A-Z].*\s\d{2},\sBirmingham,\sAL\s\d{5})', r'([a-zA-Z]\s Birmingham)', r'([a-zA-Z].*,\sBirmingham,\sAL)', r'(^Birmingham,AL$)' ] addresses = [] for pattern in address_patterns: addresses.extend(re.findall(pattern, response)) return addresses all_addresses = [] def generate_map(location_names): global all_addresses all_addresses.extend(location_names) api_key = os.environ['GOOGLEMAPS_API_KEY'] gmaps = GoogleMapsClient(key=api_key) m = folium.Map(location=[33.5175, -86.809444], zoom_start=12) for location_name in all_addresses: geocode_result = gmaps.geocode(location_name) if geocode_result: location = geocode_result[0]['geometry']['location'] folium.Marker( [location['lat'], location['lng']], tooltip=f"{geocode_result[0]['formatted_address']}" ).add_to(m) map_html = m._repr_html_() return map_html def fetch_local_news(): api_key = os.environ['SERP_API'] url = f'https://serpapi.com/search.json?engine=google_news&q=birmingham headline&api_key={api_key}' response = requests.get(url) if response.status_code == 200: results = response.json().get("news_results", []) news_html = """

Birmingham Today

""" for index, result in enumerate(results[:7]): title = result.get("title", "No title") link = result.get("link", "#") snippet = result.get("snippet", "") news_html += f"""
{index + 1}. {title}

{snippet}

""" return news_html else: return "

Failed to fetch local news

" import numpy as np import torch from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor model_id = 'openai/whisper-large-v3' device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype).to(device) processor = AutoProcessor.from_pretrained(model_id) pipe_asr = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=15, batch_size=16, torch_dtype=torch_dtype, device=device, return_timestamps=True) base_audio_drive = "/data/audio" #Normal Code with sample rate is 44100 Hz def transcribe_function(stream, new_chunk): try: sr, y = new_chunk[0], new_chunk[1] except TypeError: print(f"Error chunk structure: {type(new_chunk)}, content: {new_chunk}") return stream, "", None y = y.astype(np.float32) / np.max(np.abs(y)) if stream is not None: stream = np.concatenate([stream, y]) else: stream = y result = pipe_asr({"array": stream, "sampling_rate": sr}, return_timestamps=False) full_text = result.get("text","") return stream, full_text, result def update_map_with_response(history): if not history: return "" response = history[-1][1] addresses = extract_addresses(response) return generate_map(addresses) def clear_textbox(): return "" def show_map_if_details(history, choice): if choice in ["Details", "Conversational"]: return gr.update(visible=True), update_map_with_response(history) else: return gr.update(visible(False), "") def generate_audio_elevenlabs(text): XI_API_KEY = os.environ['ELEVENLABS_API'] VOICE_ID = 'd9MIrwLnvDeH7aZb61E9' tts_url = f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream" headers = { "Accept": "application/json", "xi-api-key": XI_API_KEY } data = { "text": str(text), "model_id": "eleven_multilingual_v2", "voice_settings": { "stability": 1.0, "similarity_boost": 0.0, "style": 0.60, "use_speaker_boost": False } } response = requests.post(tts_url, headers=headers, json=data, stream=True) if response.ok: audio_segments = [] with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f: for chunk in response.iter_content(chunk_size=1024): if chunk: f.write(chunk) audio_segments.append(chunk) temp_audio_path = f.name # Combine all audio chunks into a single file combined_audio = AudioSegment.from_file(temp_audio_path, format="mp3") combined_audio_path = os.path.join(tempfile.gettempdir(), "elevenlabs_combined_audio.mp3") combined_audio.export(combined_audio_path, format="mp3") logging.debug(f"Audio saved to {combined_audio_path}") return combined_audio_path else: logging.error(f"Error generating audio: {response.text}") return None repo_id = "parler-tts/parler-tts-mini-expresso" parler_model = ParlerTTSForConditionalGeneration.from_pretrained(repo_id).to(device) parler_tokenizer = AutoTokenizer.from_pretrained(repo_id) parler_feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id) SAMPLE_RATE = parler_feature_extractor.sampling_rate SEED = 42 def preprocess(text): number_normalizer = EnglishNumberNormalizer() text = number_normalizer(text).strip() if text[-1] not in punctuation: text = f"{text}." abbreviations_pattern = r'\b[A-Z][A-Z\.]+\b' def separate_abb(chunk): chunk = chunk.replace(".", "") return " ".join(chunk) abbreviations = re.findall(abbreviations_pattern, text) for abv in abbreviations: if abv in text: text is text.replace(abv, separate_abb(abv)) return text def chunk_text(text, max_length=250): words = text.split() chunks = [] current_chunk = [] current_length = 0 for word in words: if current_length + len(word) + 1 <= max_length: current_chunk.append(word) current_length += len(word) + 1 else: chunks.append(' '.join(current_chunk)) current_chunk = [word] current_length = len(word) + 1 if current_chunk: chunks.append(' '.join(current_chunk)) return chunks def generate_audio_parler_tts(text): description = "Thomas speaks with emphasis and excitement at a moderate pace with high quality." chunks = chunk_text(preprocess(text)) audio_segments = [] for chunk in chunks: inputs = parler_tokenizer(description, return_tensors="pt").to(device) prompt = parler_tokenizer(chunk, return_tensors="pt").to(device) set_seed(SEED) generation = parler_model.generate(input_ids=inputs.input_ids, prompt_input_ids=prompt.input_ids) audio_arr = generation.cpu().numpy().squeeze() temp_audio_path = os.path.join(tempfile.gettempdir(), f"parler_tts_audio_{len(audio_segments)}.wav") write_wav(temp_audio_path, SAMPLE_RATE, audio_arr) audio_segments.append(AudioSegment.from_wav(temp_audio_path)) combined_audio = sum(audio_segments) combined_audio_path = os.path.join(tempfile.gettempdir(), "parler_tts_combined_audio.wav") combined_audio.export(combined_audio_path, format="wav") logging.debug(f"Audio saved to {combined_audio_path}") return combined_audio_path # Load the MARS5 model mars5, config_class = torch.hub.load('Camb-ai/mars5-tts', 'mars5_english', trust_repo=True) def generate_audio_mars5(text): description = "Thomas speaks with emphasis and excitement at a moderate pace with high quality." kwargs_dict = { 'temperature': 0.2, 'top_k': -1, 'top_p': 0.2, 'typical_p': 1.0, 'freq_penalty': 2.6, 'presence_penalty': 0.4, 'rep_penalty_window': 100, 'max_prompt_phones': 360, 'deep_clone': True, 'nar_guidance_w': 3 } chunks = chunk_text(preprocess(text)) audio_segments = [] for chunk in chunks: wav = torch.zeros(1, mars5.sr) # Use a placeholder silent audio for the reference cfg = config_class(**{k: kwargs_dict[k] for k in kwargs_dict if k in config_class.__dataclass_fields__}) ar_codes, wav_out = mars5.tts(chunk, wav, "", cfg=cfg) temp_audio_path = os.path.join(tempfile.gettempdir(), f"mars5_audio_{len(audio_segments)}.wav") torchaudio.save(temp_audio_path, wav_out.unsqueeze(0), mars5.sr) audio_segments.append(AudioSegment.from_wav(temp_audio_path)) combined_audio = sum(audio_segments) combined_audio_path = os.path.join(tempfile.gettempdir(), "mars5_combined_audio.wav") combined_audio.export(combined_audio_path, format="wav") logging.debug(f"Audio saved to {combined_audio_path}") return combined_audio_path pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", torch_dtype=torch.float16) pipe.to(device) def generate_image(prompt): with torch.cuda.amp.autocast(): image = pipe( prompt, num_inference_steps=28, guidance_scale=3.0, ).images[0] return image hardcoded_prompt_1 = "Give a high quality photograph of a great looking red 2026 Toyota coupe against a skyline setting in the night, michael mann style in omaha enticing the consumer to buy this product" hardcoded_prompt_2 = "A vibrant and dynamic football game scene in the style of Peter Paul Rubens, showcasing the intense match between Alabama and Nebraska. The players are depicted with the dramatic, muscular physiques and expressive faces typical of Rubens' style. The Alabama team is wearing their iconic crimson and white uniforms, while the Nebraska team is in their classic red and white attire. The scene is filled with action, with players in mid-motion, tackling, running, and catching the ball. The background features a grand stadium filled with cheering fans, banners, and the natural landscape in the distance. The colors are rich and vibrant, with a strong use of light and shadow to create depth and drama. The overall atmosphere captures the intensity and excitement of the game, infused with the grandeur and dynamism characteristic of Rubens' work." hardcoded_prompt_3 = "Create a high-energy scene of a DJ performing on a large stage with vibrant lights, colorful lasers, a lively dancing crowd, and various electronic equipment in the background." def update_images(): image_1 = generate_image(hardcoded_prompt_1) image_2 = generate_image(hardcoded_prompt_2) image_3 = generate_image(hardcoded_prompt_3) return image_1, image_2, image_3 def fetch_local_events(): api_key = os.environ['SERP_API'] url = f'https://serpapi.com/search.json?engine=google_events&q=Events+in+Birmingham&hl=en&gl=us&api_key={api_key}' response = requests.get(url) if response.status_code == 200: events_results = response.json().get("events_results", []) events_html = """

Local Events

""" for event in events_results: title = event.get("title", "No title") date_info = event.get("date", {}) date = f"{date_info.get('start_date', '')} {date_info.get('when', '')}".replace("{", "").replace("}", "") location = event.get("address", "No location") if isinstance(location, list): location = " ".join(location) location = location.replace("[", "").replace("]", "") link = event.get("link", "#") events_html += f""" """ events_html += "
Title Date and Time Location
{title} {date} {location}
" return events_html else: return "

Failed to fetch local events

" def get_weather_icon(condition): condition_map = { "Clear": "c01d", "Partly Cloudy": "c02d", "Cloudy": "c03d", "Overcast": "c04d", "Mist": "a01d", "Patchy rain possible": "r01d", "Light rain": "r02d", "Moderate rain": "r03d", "Heavy rain": "r04d", "Snow": "s01d", "Thunderstorm": "t01d", "Fog": "a05d", } return condition_map.get(condition, "c04d") def fetch_local_weather(): try: api_key = os.environ['WEATHER_API'] url = f'https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/birmingham?unitGroup=metric&include=events%2Calerts%2Chours%2Cdays%2Ccurrent&key={api_key}' response = requests.get(url) response.raise_for_status() jsonData = response.json() current_conditions = jsonData.get("currentConditions", {}) temp_celsius = current_conditions.get("temp", "N/A") if temp_celsius != "N/A": temp_fahrenheit = int((temp_celsius * 9/5) + 32) else: temp_fahrenheit = "N/A" condition = current_conditions.get("conditions", "N/A") humidity = current_conditions.get("humidity", "N/A") weather_html = f"""

Local Weather

{condition}

Temperature: {temp_fahrenheit}°F

Condition: {condition}

Humidity: {humidity}%

""" return weather_html except requests.exceptions.RequestException as e: return f"

Failed to fetch local weather: {e}

" def handle_retrieval_mode_change(choice): if choice == "KGF": return gr.update(interactive=False), gr.update(interactive=False) else: return gr.update(interactive=True), gr.update(interactive=True) def fetch_yelp_restaurants(): from serpapi.google_search import GoogleSearch import os params = { "engine": "yelp", "find_desc": "Restaurant", "find_loc": "Birmingham, AL, USA", "api_key": os.getenv("SERP_API") } search = GoogleSearch(params) results = search.get_dict() organic_results = results.get("organic_results", []) def star_rating(rating): full_stars = int(float(rating)) half_star = 1 if (float(rating) - full_stars) >= 0.5 else 0 empty_stars = 5 - full_stars - half_star stars = "★" * full_stars + "½" * half_star + "☆" * empty_stars return stars response_text = "" for result in organic_results[:5]: # Limiting to top 10 restaurants name = result.get("title", "No name") rating = result.get("rating", "No rating") reviews = result.get("reviews", "No reviews") phone = result.get("phone", "Not Available") snippet = result.get("snippet", "Not Available") location = f"{name}, Birmingham, AL" link = result.get("link", "#") if isinstance(snippet, list): snippet = " | ".join(snippet[:5]) # Limiting to top 5 snippets # Format the output for each restaurant response_text += f"[{name}]({link})\n" # Name with clickable link, no bold response_text += f"*{location}*\n" # Location with restaurant name and "Birmingham, AL" response_text += f"**Contact No:** {phone}\n" response_text += f"**Rating:** {star_rating(rating)} ({rating} stars, {reviews} reviews)\n" response_text += f"**Snippet:** {snippet}\n" response_text += "-" * 50 + "\n" return response_text def fetch_google_hotels(query="Birmingham Hotel", check_in="2024-08-14", check_out="2024-08-15", adults=2): params = { "engine": "google_hotels", "q": query, "check_in_date": check_in, "check_out_date": check_out, "adults": str(adults), "currency": "USD", "gl": "us", "hl": "en", "api_key": os.getenv("SERP_API") } search = GoogleSearch(params) results = search.get_dict() hotel_results = results.get("properties", []) # hotel_info = "" # for hotel in hotel_results[:5]: # Limiting to top 5 hotels # name = hotel.get('name', 'No name') # description = hotel.get('description', 'No description') # link = hotel.get('link', '#') # check_in_time = hotel.get('check_in_time', 'N/A') # check_out_time = hotel.get('check_out_time', 'N/A') # rate_per_night = hotel.get('rate_per_night', {}).get('lowest', 'N/A') # before_taxes_fees = hotel.get('rate_per_night', {}).get('before_taxes_fees', 'N/A') # total_rate = hotel.get('total_rate', {}).get('lowest', 'N/A') # deal = hotel.get('deal', 'N/A') # deal_description = hotel.get('deal_description', 'N/A') # nearby_places = hotel.get('nearby_places', []) # amenities = hotel.get('amenities', []) # hotel_info += f"**Hotel Name:** [{name}]({link})\n" # hotel_info += f"**Description:** {description}\n" # hotel_info += f"**Check-in Time:** {check_in_time}\n" # hotel_info += f"**Check-out Time:** {check_out_time}\n" # hotel_info += f"**Rate per Night:** {rate_per_night} (Before taxes/fees: {before_taxes_fees})\n" # hotel_info += f"**Total Rate:** {total_rate}\n" # hotel_info += f"**Deal:** {deal} ({deal_description})\n" # if nearby_places: # hotel_info += "**Nearby Places:**\n" # for place in nearby_places: # place_name = place.get('name', 'Unknown Place') # transportations = place.get('transportations', []) # hotel_info += f" - {place_name}:\n" # for transport in transportations: # transport_type = transport.get('type', 'N/A') # duration = transport.get('duration', 'N/A') # hotel_info += f" - {transport_type}: {duration}\n" # if amenities: # hotel_info += "**Amenities:**\n" # hotel_info += ", ".join(amenities) + "\n" # hotel_info += "-" * 50 + "\n" # return hotel_info hotel_info = "" for hotel in hotel_results[:5]: # Limiting to top 5 hotels name = hotel.get('name', 'No name') description = hotel.get('description', 'No description') link = hotel.get('link', '#') check_in_time = hotel.get('check_in_time', 'N/A') check_out_time = hotel.get('check_out_time', 'N/A') rate_per_night = hotel.get('rate_per_night', {}).get('lowest', 'N/A') before_taxes_fees = hotel.get('rate_per_night', {}).get('before_taxes_fees', 'N/A') total_rate = hotel.get('total_rate', {}).get('lowest', 'N/A') deal = hotel.get('deal', 'N/A') deal_description = hotel.get('deal_description', 'N/A') nearby_places = hotel.get('nearby_places', []) amenities = hotel.get('amenities', []) # Adding the "Location" field location = f"{name}, Birmingham, AL" hotel_info += f"**Hotel Name:** [{name}]({link})\n" hotel_info += f"**Location:** {location}\n" hotel_info += f"**Description:** {description}\n" hotel_info += f"**Check-in Time:** {check_in_time}\n" hotel_info += f"**Check-out Time:** {check_out_time}\n" hotel_info += f"**Rate per Night:** {rate_per_night} (Before taxes/fees: {before_taxes_fees})\n" hotel_info += f"**Total Rate:** {total_rate}\n" hotel_info += f"**Deal:** {deal} ({deal_description})\n" if nearby_places: hotel_info += "**Nearby Places:**\n" for place in nearby_places: place_name = place.get('name', 'Unknown Place') transportations = place.get('transportations', []) hotel_info += f" - {place_name}:\n" for transport in transportations: transport_type = transport.get('type', 'N/A') duration = transport.get('duration', 'N/A') hotel_info += f" - {transport_type}: {duration}\n" if amenities: hotel_info += "**Amenities:**\n" hotel_info += ", ".join(amenities) + "\n" hotel_info += "-" * 50 + "\n" return hotel_info def fetch_google_flights(departure_id="JFK", arrival_id="BHM", outbound_date="2024-08-14", return_date="2024-08-20"): from serpapi import GoogleSearch params = { "engine": "google_flights", "departure_id": "PEK", "arrival_id": "AUS", "outbound_date": "2024-08-14", "return_date": "2024-08-20", "currency": "USD", "hl": "en", "api_key": os.getenv("SERP_API") # Replace with your actual API key } search = GoogleSearch(params) results = search.get_dict() # Extract flight details from the results best_flights = results.get('best_flights', []) formatted_flights = [] # Process each flight in the best_flights list for i, flight in enumerate(best_flights, start=1): flight_info = f"Flight {i}:\n" for segment in flight.get('flights', []): departure_airport = segment.get('departure_airport', {}).get('name', 'Unknown Departure Airport') departure_time = segment.get('departure_airport', {}).get('time', 'Unknown Time') arrival_airport = segment.get('arrival_airport', {}).get('name', 'Unknown Arrival Airport') arrival_time = segment.get('arrival_airport', {}).get('time', 'Unknown Time') duration = segment.get('duration', 'Unknown Duration') airplane = segment.get('airplane', 'Unknown Airplane') # Format the flight segment details flight_info += f" - Departure: {departure_airport} at {departure_time}\n" flight_info += f" - Arrival: {arrival_airport} at {arrival_time}\n" flight_info += f" - Duration: {duration} minutes\n" flight_info += f" - Airplane: {airplane}\n" formatted_flights.append(flight_info) # Combine the formatted flight information into a single output final_output = "Here are some available flights for today:\n\n" + "\n".join(formatted_flights) return final_output with gr.Blocks(theme='Pijush2023/scikit-learn-pijush') as demo: with gr.Row(): with gr.Column(): state = gr.State() chatbot = gr.Chatbot([], elem_id="RADAR:Channel 94.1", bubble_full_width=False) choice = gr.Radio(label="Select Style", choices=["Details", "Conversational"], value="Conversational") retrieval_mode = gr.Radio(label="Retrieval Mode", choices=["VDB", "KGF"], value="VDB") gr.Markdown("

Talk to RADAR

", elem_id="voice-markdown") chat_input = gr.Textbox(show_copy_button=True, interactive=True, show_label=False, label="ASK Radar !!!", placeholder="After Prompt,click Retriever Only") tts_choice = gr.Radio(label="Select TTS System", choices=["Alpha", "Beta", "Gamma"], value="Alpha") retriever_button = gr.Button("Retriever") clear_button = gr.Button("Clear") clear_button.click(lambda:[None,None] ,outputs=[chat_input, state]) gr.Markdown("

Radar Map

", elem_id="Map-Radar") location_output = gr.HTML() # Define a single audio component audio_output = gr.Audio(interactive=False, autoplay=True) def stop_audio(): audio_output.stop() return None # Define the sequence of actions for the "Retriever" button retriever_sequence = ( retriever_button.click(fn=stop_audio, inputs=[], outputs=[audio_output],api_name="Ask_Retriever") .then(fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input],api_name="voice_query") .then(fn=bot, inputs=[chatbot, choice, tts_choice, retrieval_mode], outputs=[chatbot, audio_output],api_name="generate_voice_response" ) .then(fn=show_map_if_details, inputs=[chatbot, choice], outputs=[location_output, location_output], api_name="map_finder") .then(fn=clear_textbox, inputs=[], outputs=[chat_input]) ) # Link the "Enter" key (submit event) to the same sequence of actions chat_input.submit(fn=stop_audio, inputs=[], outputs=[audio_output]) chat_input.submit(fn=add_message, inputs=[chatbot, chat_input], outputs=[chatbot, chat_input],api_name="voice_query").then( fn=bot, inputs=[chatbot, choice, tts_choice, retrieval_mode], outputs=[chatbot, audio_output], api_name="generate_voice_response" ).then( fn=show_map_if_details, inputs=[chatbot, choice], outputs=[location_output, location_output], api_name="map_finder" ).then( fn=clear_textbox, inputs=[], outputs=[chat_input] ) audio_input = gr.Audio(sources=["microphone"], streaming=True, type='numpy', every=0.1) audio_input.stream(transcribe_function, inputs=[state, audio_input], outputs=[state, chat_input], api_name="voice_query_to_text") # Handle retrieval mode change retrieval_mode.change(fn=handle_retrieval_mode_change, inputs=retrieval_mode, outputs=[choice, choice]) with gr.Column(): weather_output = gr.HTML(value=fetch_local_weather()) news_output = gr.HTML(value=fetch_local_news()) events_output = gr.HTML(value=fetch_local_events()) # restaurant_output=gr.HTML(value=fetch_yelp_restaurants()) with gr.Column(): image_output_1 = gr.Image(value=generate_image(hardcoded_prompt_1), width=400, height=400) image_output_2 = gr.Image(value=generate_image(hardcoded_prompt_2), width=400, height=400) image_output_3 = gr.Image(value=generate_image(hardcoded_prompt_3), width=400, height=400) refresh_button = gr.Button("Refresh Images") refresh_button.click(fn=update_images, inputs=None, outputs=[image_output_1, image_output_2, image_output_3], api_name="update_image") demo.queue() demo.launch(share=True)