app.py

# import gradio as gr
# from huggingface_hub import InferenceClient

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

# # Step 2: Set up your InferenceClient (same as before)
# client = InferenceClient("google/gemma-2-2b-jpn-it")
# # HuggingFaceH4/zephyr-7b-beta
# 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()

import gradio as gr
from huggingface_hub import InferenceClient

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

# Step 2: Set up your InferenceClient (using text-generation instead of chat)
client = InferenceClient("google/gemma-2-2b-jpn-it")

def respond(
    message,
    history: list[dict],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
):
    """
    Merges 'system_message', 'background_text', and conversation 'history' 
    into a single text prompt, then calls client.text_generation(...) 
    for a response.
    """
    if history is None:
        history = []

    # Combine system instructions + background + prior conversation + new user message
    prompt = f"{system_message}\n\n### Background Information ###\n{background_text}\n\n"
    for interaction in history:
        if "user" in interaction:
            prompt += f"User: {interaction['user']}\n"
        if "assistant" in interaction:
            prompt += f"Assistant: {interaction['assistant']}\n"
    # Add the latest user query
    prompt += f"User: {message}\nAssistant:"  # We'll generate the Assistant's text after this

    # Generate response using text_generation in streaming mode
    response = ""
    # The text returned will include the entire prompt + new text,
    # so we’ll need to subtract out the prompt length to isolate the new portion.
    prompt_length = len(prompt)

    for chunk in client.text_generation(
        prompt=prompt,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        stream=True,  # streaming each chunk
    ):
        # Each chunk is a dict like {"generated_text": "full text so far..."}
        full_text = chunk["generated_text"]
        # The newly generated portion is what's after the original prompt
        new_text = full_text[prompt_length:]
        response += new_text
        prompt_length = len(full_text)  # update for next chunk
        yield response

    # For debugging: show what we actually sent
    print("----- FULL PROMPT -----")
    print(prompt)
    print("----- END PROMPT -----")


# Step 3: Build a Gradio Blocks interface with two Tabs
with gr.Blocks() as demo:
    with gr.Tab("Gemma 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",  # Gradio will keep track of (user, assistant) messages in history
        )

    # Optional: If you want a separate tab to display background_text
    # with gr.Tab("Varun's Background"):
    #     gr.Markdown("# About Varun")
    #     gr.Markdown(background_text)

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