app.py

import gradio as gr
import httpx
import asyncio
import json

# Replace with your NEW Modal API endpoint URL (for the non-streaming backend)
MODAL_API_ENDPOINT = "https://blastingneurons--collective-hive-backend-monolithic-fina-30e6e4.modal.run"

# Helper function to format chat history for Gradio's 'messages' type
def format_chat_history_for_gradio(log_entries: list[dict]) -> list[dict]:
    formatted_messages = []
    for entry in log_entries:
        role = entry.get("agent", "System") 
        content = entry.get("text", "")
        formatted_messages.append({"role": role, "content": content})
    return formatted_messages

async def call_modal_backend_sync(problem_input: str, complexity: int):
    # Initial yield to clear previous state and show connecting message
    yield (
        "Connecting to Hive...",
        format_chat_history_for_gradio([]),
        "", "", ""
    )

    try:
        async with httpx.AsyncClient(timeout=600.0) as client: # Longer timeout for the full process
            response = await client.post(MODAL_API_ENDPOINT, json={"problem": problem_input, "complexity": complexity})
            response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx)
            
            response_data = response.json() # Parse the full JSON response
            
            final_status = response_data.get("status", "Unknown Status")
            final_chat_history = response_data.get("chat_history", [])
            final_solution = response_data.get("solution", "No solution provided.")
            final_confidence = response_data.get("confidence", "0.0%")
            final_minority_opinions = response_data.get("minority_opinions", "None")

            yield (
                final_status,
                format_chat_history_for_gradio(final_chat_history),
                final_solution,
                final_confidence,
                final_minority_opinions
            )
            return # Done processing

    except httpx.HTTPStatusError as e:
        error_message = f"HTTP Error from Modal backend: {e.response.status_code} - {e.response.text}"
        print(error_message)
        yield (error_message, format_chat_history_for_gradio([]), "", "", "")
    except httpx.RequestError as e:
        error_message = f"Request Error: Could not connect to Modal backend: {e}"
        print(error_message)
        yield (error_message, format_chat_history_for_gradio([]), "", "", "")
    except Exception as e:
        error_message = f"An unexpected error occurred during API call: {e}"
        print(error_message)
        yield (error_message, format_chat_history_for_gradio([]), "", "", "")

# Fallback yield in case of unexpected termination before return
# yield ("An unexpected error occurred and processing stopped.", format_chat_history_for_gradio([]), "", "", "")


with gr.Blocks() as demo:
    gr.Markdown("# Collective Intelligence Hive")
    gr.Markdown("Enter a problem and watch a hive of AI agents collaborate to solve it! Powered by Modal and Nebius.")

    with gr.Row():
        problem_input = gr.Textbox(label="Problem to Solve", lines=3, placeholder="e.g., 'Develop a marketing strategy for a new eco-friendly smart home device targeting millennials.'", scale=3)
        complexity_slider = gr.Slider(minimum=1, maximum=5, value=3, step=1, label="Problem Complexity", scale=1)
        
    initiate_btn = gr.Button("Initiate Hive", variant="primary")

    status_output = gr.Textbox(label="Hive Status", interactive=False)
    
    with gr.Row():
        with gr.Column(scale=2):
            chat_display = gr.Chatbot(
                label="Agent Discussion Log",
                height=500,
                type='messages',
                autoscroll=True
            )
            
        with gr.Column(scale=1):
            solution_output = gr.Textbox(label="Synthesized Solution", lines=10, interactive=False)
            confidence_output = gr.Textbox(label="Solution Confidence", interactive=False)
            minority_output = gr.Textbox(label="Minority Opinions", lines=3, interactive=False)

    initiate_btn.click(
        call_modal_backend_sync, # Changed function name
        inputs=[problem_input, complexity_slider],
        outputs=[
            status_output,
            chat_display,
            solution_output,
            confidence_output,
            minority_output
        ],
        queue=True
    )

demo.launch()