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

client = InferenceClient("google/gemma-2-2b-jpn-it")

def respond(message, history, system_message, max_tokens, temperature, top_p):
    if history is None:
        history = []

    prompt = f"{system_message}\n\n# Background...\n\n"  # etc.
    # Build up your prompt from history...

    response = ""
    for chunk in client.text_generation(
        prompt=prompt,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        stream=True,
    ):
        # 'chunk' is a string of newly generated text.
        response += chunk
        yield response

    # (Optional) log the final prompt
    print("PROMPT:", prompt)


with gr.Blocks() as demo:
    with gr.Tab("Gemma Chat Agent"):
        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"),
            ],
            type="messages",
        )

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