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# app.py (on Hugging Face Spaces)
import gradio as gr
import httpx
import asyncio
import json
# Replace with your Modal API endpoint URL
MODAL_API_ENDPOINT = "https://blastingneurons--collective-hive-backend-orchestrate-hive-api.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:
# Default to 'System' if agent name is not found
role = entry.get("agent", "System")
content = entry.get("text", "")
formatted_messages.append({"role": role, "content": content})
return formatted_messages
async def call_modal_backend(problem_input: str, complexity: int):
full_chat_history = []
# Initial yield to clear previous state and show connecting message
yield {
"status": "Connecting to Hive...",
"chat_history": [],
"solution": "", "confidence": "", "minority_opinions": ""
}
try:
async with httpx.AsyncClient(timeout=600.0) as client: # Longer timeout for the full process
async with client.stream("POST", MODAL_API_ENDPOINT, json={"problem": problem_input, "complexity": complexity}) as response:
response.raise_for_status() # Raise an exception for HTTP errors (4xx or 5xx)
# We need to buffer chunks to ensure we parse complete JSON lines
buffer = ""
async for chunk in response.aiter_bytes():
buffer += chunk.decode('utf-8')
while "\n" in buffer:
line, buffer = buffer.split("\n", 1)
if not line.strip(): continue # Skip empty lines
try:
data = json.loads(line)
event_type = data.get("event")
if event_type == "status_update":
yield {
"status": data["data"],
"chat_history": format_chat_history_for_gradio(full_chat_history)
}
elif event_type == "chat_update":
full_chat_history.append(data["data"])
yield {
"status": "In Progress...",
"chat_history": format_chat_history_for_gradio(full_chat_history)
}
elif event_type == "final_solution":
yield {
"status": "Solution Complete!",
"chat_history": format_chat_history_for_gradio(full_chat_history + [{"agent": "System", "text": "Final solution synthesized."}]),
"solution": data["solution"],
"confidence": data["confidence"],
"minority_opinions": data["minority_opinions"]
}
return # Done processing
except json.JSONDecodeError as e:
print(f"JSON Decode Error: {e} in line: {line}")
# This could happen if a partial JSON is received.
# The buffering logic should help, but if it's consistently failing, check Modal's streaming output.
except Exception as e:
print(f"Error processing event: {e}, Data: {data}")
yield {"status": f"Error: {e}", "chat_history": format_chat_history_for_gradio(full_chat_history)}
return
except httpx.HTTPStatusError as e:
error_message = f"HTTP Error: {e.response.status_code} - {e.response.text}"
print(error_message)
yield {"status": error_message, "chat_history": format_chat_history_for_gradio(full_chat_history)}
except httpx.RequestError as e:
error_message = f"Request Error: Could not connect to Modal backend: {e}"
print(error_message)
yield {"status": error_message, "chat_history": format_chat_history_for_gradio(full_chat_history)}
except Exception as e:
error_message = f"An unexpected error occurred: {e}"
print(error_message)
yield {"status": error_message, "chat_history": format_chat_history_for_gradio(full_chat_history)}
yield {"status": "Process finished unexpectedly or ended.", "chat_history": format_chat_history_for_gradio(full_chat_history)}
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,
inputs=[problem_input, complexity_slider],
outputs=[
status_output,
chat_display,
solution_output,
confidence_output,
minority_output
],
queue=True
)
demo.launch() |