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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() |