Delete app.py

app.py

@@ -1,109 +0,0 @@
-"""
-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
-    
-    @app.post("/api/gaia")
-    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)