app.py

import gradio as gr
from run_chatbot import process_with_longcepo, SAMBANOVA_MODEL

def chatbot_interface(system_prompt, context, query):
    """Gradio interface function to interact with the LongCePO chatbot."""
    if not context or not query:
        return "Please provide both context and query."
    
    # Combine context and query using the expected delimiter
    initial_query = f"{context}<CONTEXT_END>{query}"
    
    # Use a default system prompt if none is provided
    if not system_prompt:
        system_prompt = "You are a helpful assistant designed to answer questions based on the provided context."
        
    print(f"Received request:\nSystem Prompt: {system_prompt}\nContext: {context[:100]}...\nQuery: {query}")
    
    # Call the processing function
    result = process_with_longcepo(system_prompt, initial_query)
    
    print(f"Returning result: {result[:100]}...")
    return result

# Define Gradio interface components
iface = gr.Interface(
    fn=chatbot_interface,
    inputs=[
        gr.Textbox(label="System Prompt (Optional)", placeholder="Enter system prompt here...", lines=2),
        gr.Textbox(label="Context", placeholder="Enter the long context here...", lines=10),
        gr.Textbox(label="Query", placeholder="Enter your query based on the context here...", lines=2)
    ],
    outputs=gr.Textbox(label="Answer", lines=10),
    title=f"LongCePO Chatbot ({SAMBANOVA_MODEL})",
    description="Enter a long context and a query. The chatbot will use the LongCePO method with Sambanova backend to generate an answer.",
    allow_flagging="never"
)

# Launch the Gradio app
if __name__ == "__main__":
    print("Launching Gradio interface...")
    # Listen on 0.0.0.0 to make it accessible externally if needed
    iface.launch(server_name="0.0.0.0", server_port=7860)