import gradio as gr
from openai import OpenAI
import os
from fpdf import FPDF
from docx import Document

css = '''
.gradio-container{max-width: 1000px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

ACCESS_TOKEN = os.getenv("HF_TOKEN")

client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1/",
    api_key=ACCESS_TOKEN,
)

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.completions.create(
        model="meta-llama/Meta-Llama-3.1-8B-Instruct",
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
        messages=messages,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

def save_to_file(history, file_format):
    if file_format == "PDF":
        pdf = FPDF()
        pdf.add_page()
        pdf.set_auto_page_break(auto=True, margin=15)
        pdf.set_font("Arial", size=12)
        for user_message, assistant_message in history:
            pdf.multi_cell(0, 10, f"User: {user_message}")
            pdf.multi_cell(0, 10, f"Assistant: {assistant_message}")
        file_name = "chat_history.pdf"
        pdf.output(file_name)
        
    elif file_format == "DOCX":
        doc = Document()
        for user_message, assistant_message in history:
            doc.add_paragraph(f"User: {user_message}")
            doc.add_paragraph(f"Assistant: {assistant_message}")
        file_name = "chat_history.docx"
        doc.save(file_name)
        
    elif file_format == "TXT":
        file_name = "chat_history.txt"
        with open(file_name, "w") as file:
            for user_message, assistant_message in history:
                file.write(f"User: {user_message}\n")
                file.write(f"Assistant: {assistant_message}\n")
                
    return file_name

# Gradio Interface Setup
with gr.Blocks(css=css) as demo:
    system_message = gr.Textbox(value="", label="System message")
    max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
    temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
    top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-P")
    save_as = gr.Radio(["PDF", "DOCX", "TXT"], label="Save As")
    
    chat = gr.Chatbot()
    msg = gr.Textbox(label="Your message")

    def respond_wrapper(message, history):
        response_generator = respond(
            message,
            history,
            system_message.value,
            max_tokens.value,
            temperature.value,
            top_p.value
        )
        response = next(response_generator)
        return history + [(message, response)]

    msg.submit(respond_wrapper, [msg, chat], [chat])
    
    save_button = gr.Button("Save Conversation")
    output_file = gr.File(label="Download File")
    
    def handle_save(history, file_format):
        return save_to_file(history, file_format)

    save_button.click(handle_save, inputs=[chat, save_as], outputs=output_file)

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