app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Step 1: Read your background info
with open("BACKGROUND_NEW.md", "r", encoding="utf-8") as f:
    background_text = f.read()

# Step 2: Set up your InferenceClient (same as before)
client = InferenceClient("bunnycore/QwQen-3B-LCoT")
# HuggingFaceH4/zephyr-7b-beta
# meta-llama/Llama-3.2-1B
def respond(
    message,
    history: list[dict],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
):
    if history is None:
        history = []

    # Include background text as part of the system message for context
    combined_system_message = f"{system_message}\n\n### Background Information ###\n{background_text}"

    # Start building the conversation history
    messages = [{"role": "system", "content": combined_system_message}]
    
    # Add conversation history
    for interaction in history:
        if "user" in interaction:
            messages.append({"role": "user", "content": interaction["user"]})
        if "assistant" in interaction:
            messages.append({"role": "assistant", "content": interaction["assistant"]})

    # Add the latest user message
    messages.append({"role": "user", "content": message})

    # Generate response
    response = ""
    for msg in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        
        token = msg.choices[0].delta.content
        response += token
        yield response
    # print("----- SYSTEM MESSAGE -----")
    # print(messages[0]["content"])
    # print("----- FULL MESSAGES LIST -----")
    # for m in messages:
    #     print(m)
    # print("-------------------------")
    
# Step 3: Build a Gradio Blocks interface with two Tabs
with gr.Blocks() as demo:
    # Tab 1: GPT Chat Agent
    with gr.Tab("GPT Chat Agent"):
        gr.Markdown("## Welcome to Varun's GPT Agent")
        gr.Markdown("Feel free to ask questions about Varun’s journey, skills, and more!")
        chat = gr.ChatInterface(
            fn=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)"),
            ],
            type="messages",  # Specify message type
        )

    # # Tab 2: Background Document
    # with gr.Tab("Varun's Background"):
    #     gr.Markdown("# About Varun")
    #     gr.Markdown(background_text)

# Step 4: Launch
if __name__ == "__main__":
    demo.launch()