import gradio as gr
from huggingface_hub import InferenceClient
import pandas as pd
import json
import os
import re
import uuid


client = InferenceClient("tiiuae/falcon-7b-instruct") #  HuggingFaceH4/zephyr-7b-beta


def trigger_example(example):
    chat, updated_history = generate_response(example)
    return chat, updated_history

    
def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    uploaded_file,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    if uploaded_file is not None:
        with open(uploaded_file.name, "r") as f:
            file_content = f.read()
        messages.append({"role": "user", "content": f"{message}\n\nFile content:\n{file_content}"})
    else:
        messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response

  
    if uploaded_file is not None:
        print(f"Uploaded file: {uploaded_file.name}")


        if uploaded_file.name.endswith(".csv"):
            try:
                df = pd.read_csv(uploaded_file.name)
                print(f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.")
                json_data = df.to_json(orient="records")
                with open(f"{uploaded_file.name.split('.')[0]}.json", "w") as json_file:
                    json_file.write(json_data)
                print(f"JSON file created: {uploaded_file.name.split('.')[0]}.json")
            except Exception as e:
                print(f"Error loading CSV file: {e}")

        elif uploaded_file.name.endswith(".txt"):
            try:
                with open(uploaded_file.name, "r") as f:
                    text = f.read()
                print(f"Text file loaded with {len(text)} characters.")
                json_data = json.dumps({"text": text})
                with open(f"{uploaded_file.name.split('.')[0]}.json", "w") as json_file:
                    json_file.write(json_data)
                print(f"JSON file created: {uploaded_file.name.split('.')[0]}.json")
            except Exception as e:
                print(f"Error loading text file: {e}")

def clear_chat():
    return [], [], str(uuid.uuid4())


examples = [
    "Explain the relativity theory in French",
    "Como sair de um helicóptero que caiu na água?",
    "¿Cómo le explicarías el aprendizaje automático a un extraterrestre?",
    "Explain gravity to a chicken.",
    "Give me an example of an endangered species and let me know what I can do to help preserve it",
    "Formally introduce the transformer architecture with notation.",
    
]



demo = gr.ChatInterface(
    respond,
    title="Nixie Steamcore, a hotbot!",
    additional_inputs=[
        gr.Textbox(value="Nixie Steamcore, a hotbot!", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=1.2, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
        gr.File(label="Upload a document"),
    ],
)

if __name__ == "__main__":
    demo.launch(debug=True)

    """
if __name__ == "__main__":
    # demo.launch(debug=True)
    try:
        demo.queue(api_open=False, max_size=40).launch(show_api=False)
    except Exception as e:
        print(f"Error: {e}")
"""

"""
import gradio as gr
from huggingface_hub import InferenceClient

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response

demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()
    """