import streamlit as st
import os
import requests

from PIL import Image
#from pydub.playback import Audio
from pydub import AudioSegment

import IPython
import soundfile as sf

def play_audio(audio):
    sf.write("speech_converted.wav", audio.numpy(), samplerate=16000)
    return IPython.display.Audio("speech_converted.wav")
    

# From transformers import BertModel, BertTokenizer
from transformers import HfAgent, load_tool

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, Agent, LocalAgent

# checkpoint = "THUDM/agentlm-7b"
# model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
# tokenizer = AutoTokenizer.from_pretrained(checkpoint)

# agent = LocalAgent(model, tokenizer)
# agent.run("Draw me a picture of rivers and lakes.")

# print(agent.run("Is the following `text` (in Spanish) positive or negative?", text="¡Este es un API muy agradable!"))

# Load tools
random_character_tool = load_tool("Chris4K/random-character-tool")
text_generation_tool = load_tool("Chris4K/text-generation-tool")


tools = [random_character_tool, text_generation_tool]

# Define the custom HfAgent class
class CustomHfAgent(Agent):
    def __init__(
        self, url_endpoint, token=os.environ['HF_token'], chat_prompt_template=None, run_prompt_template=None, additional_tools=None, input_params=None
    ):
        super().__init__(
            chat_prompt_template=chat_prompt_template,
            run_prompt_template=run_prompt_template,
            additional_tools=additional_tools,
        )
        self.url_endpoint = url_endpoint
        self.token = token
        self.input_params = input_params

    def generate_one(self, prompt, stop):
        headers = {"Authorization": self.token}
        # Use the value from input_params or a default value if not provided
        max_new_tokens = self.input_params.get("max_new_tokens", 192)
        inputs = {
            "inputs": prompt,
            # Here the max_new_token varies from default 200 which leads to an error
            "parameters": {"max_new_tokens": max_new_tokens, "return_full_text": False, "stop": stop},
        }
        response = requests.post(self.url_endpoint, json=inputs, headers=headers)
        if response.status_code == 429:
            print("Getting rate-limited, waiting a tiny bit before trying again.")
            time.sleep(1)
            return self._generate_one(prompt)
        elif response.status_code != 200:
            raise ValueError(f"Errors {inputs} {response.status_code}: {response.json()}")
        print(response)
        result = response.json()[0]["generated_text"]
        # Inference API returns the stop sequence
        for stop_seq in stop:
            if result.endswith(stop_seq):
                return result[: -len(stop_seq)]
        return result

st.title("Hugging Face Agent and tools")

# Input field for the user's message
message = st.text_input("Enter your message:", "")

# Checkboxes for the tools to be used by the agent


tool_checkboxes = [st.checkbox(f"Use {tool.name} --- {tool.description} ") for tool in tools]

# Submit button
#submit_button = st.button("Submit")

# Initialize the agent
agent = CustomHfAgent(
    url_endpoint="https://api-inference.huggingface.co/models/bigcode/starcoder",
    token=os.environ['HF_token'],
    additional_tools=selected_tools,
    input_params={"max_new_tokens": 192},  # Set the desired value
)


# Define the callback function to handle the form submission
def handle_submission():
    # Get the user's message and the selected tools
    #message = st.text_input("Enter your message:", "")
    
    #selected_tools = []
    selected_tools = [tool for idx, tool in enumerate(tools) if tool_checkboxes[idx]]

    print(selected_tools)

    agent.tools = selected_tools

    
    # Run the agent with the user's message and selected tools
    #response = agent.run(message)
    response = agent.chat(message)

    print("Response " + response)
    
    # Display the agent's response
    if response is None:
        st.warning("The agent's response is None.  Please try again. For Example: Generate an image of a boat in the water")
    elif isinstance(response, Image.Image):
        # Display the image response
        st.image(response)
    elif "audio" in response:
        # Handle audio response (replace with your audio rendering code)
        audio_data = base64.b64decode(response.split(",")[1])
        audio = AudioSegment.from_file(io.BytesIO(audio_data))
        st.audio(audio)
    elif isinstance(response, AudioSegment):
        # Handle audio response (replace with your audio rendering code)
        st.audio(response)
    elif isinstance(response, str):
        # Display the text response
        st.write(response)
    elif "text" in response:
        # Display the text response
        st.write(response)
    else:
        # Handle unrecognized response type
        st.warning("Unrecognized response type. Please try again. For Example: Generate an image of a boat in the water")



# Add the callback function to the Streamlit app
submit_button = st.button("Submit", on_click=handle_submission)

# Define a callback function to handle the button click
def ask_again():
    # Reset the message input field
    message_input.value = ""

    # Run the agent again with an empty message
    agent.run("")

# Add the callback function to the button
ask_again = st.button("Ask again", on_click=ask_again)