Spaces:
Configuration error
Configuration error
| """ | |
| Hugging Face Space implementation for Personal Task Manager Agent | |
| This file serves as the entry point for the Hugging Face Space | |
| """ | |
| import gradio as gr | |
| from task_manager_agent import TaskManagerAgent | |
| import json | |
| import os | |
| # Initialize the agent | |
| agent = TaskManagerAgent() | |
| # Try to load existing tasks if available | |
| if os.path.exists("tasks.json"): | |
| agent.load_state("tasks.json") | |
| def process_message(message, history): | |
| """Process user message and return agent response""" | |
| response = agent.process_query(message) | |
| # Save state after each interaction | |
| agent.save_state("tasks.json") | |
| return response | |
| def get_gaia_answer(question): | |
| """ | |
| Function to process GAIA benchmark questions | |
| This is the function that will be called by the GAIA API | |
| """ | |
| # Process the question with our agent | |
| response = agent.process_query(question) | |
| # For GAIA benchmark, we need to return just the answer without any formatting | |
| # Strip any extra formatting that might be in the response | |
| clean_response = response.strip() | |
| return clean_response | |
| # Create Gradio interface | |
| with gr.Blocks(title="Personal Task Manager Agent") as demo: | |
| gr.Markdown("# Personal Task Manager Agent") | |
| gr.Markdown(""" | |
| This agent helps you manage tasks through natural language commands. | |
| ## Example commands: | |
| - Add a task: "Add a new task to buy groceries" | |
| - Add with details: "Add task to call mom priority:high due:2023-05-20 category:personal" | |
| - List tasks: "Show me my tasks" or "What do I need to do?" | |
| - Complete a task: "Mark task 2 as done" or "I completed task 3" | |
| - Delete a task: "Delete task 1" or "Remove task 4" | |
| - Filter tasks: "Show high priority tasks" or "List personal tasks" | |
| - Get help: "Help me" or "What can you do?" | |
| """) | |
| chatbot = gr.Chatbot(height=400) | |
| msg = gr.Textbox(label="Type your command here") | |
| clear = gr.Button("Clear") | |
| def user(message, history): | |
| return "", history + [[message, None]] | |
| def bot(history): | |
| message = history[-1][0] | |
| response = process_message(message, history) | |
| history[-1][1] = response | |
| return history | |
| msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then( | |
| bot, chatbot, chatbot | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| # Add GAIA API endpoint explanation | |
| gr.Markdown(""" | |
| ## GAIA Benchmark API | |
| This Space includes an API endpoint for the GAIA benchmark. The API processes questions | |
| and returns answers in the format expected by the benchmark. | |
| The endpoint is automatically available when deployed on Hugging Face Spaces. | |
| """) | |
| # For GAIA API endpoint | |
| def gaia_api(question): | |
| """API endpoint for GAIA benchmark""" | |
| answer = get_gaia_answer(question) | |
| return {"answer": answer} | |
| # Launch the app | |
| if __name__ == "__main__": | |
| # Set up FastAPI for GAIA benchmark | |
| from fastapi import FastAPI, Request | |
| import uvicorn | |
| from pydantic import BaseModel | |
| app = FastAPI() | |
| class Question(BaseModel): | |
| question: str | |
| async def api_gaia(question: Question): | |
| return gaia_api(question.question) | |
| # Mount Gradio app | |
| demo.launch(server_name="0.0.0.0", server_port=7860) | |