Create app.py

app.py

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+import os
+import gradio as gr
+import requests
+import pandas as pd
+from typing import Dict, List
+import asyncio
+
+# custom imports
+from agents import Agent
+from tool import get_tools
+from model import get_model
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+MODEL_ID = "groq/llama-3.3-70b-versatile"  # Groq's fastest model
+RATE_LIMIT_DELAY = 1  # Groq has generous rate limits
+
+
+# --- Async Question Processing ---
+async def process_question(agent, question: str, task_id: str) -> Dict:
+    """Process a single question and return both answer AND full log entry"""
+    try:
+        answer = agent(question)
+        return {
+            "submission": {"task_id": task_id, "submitted_answer": answer},
+            "log": {"Task ID": task_id, "Question": question, "Submitted Answer": answer}
+        }
+    except Exception as e:
+        error_msg = f"ERROR: {str(e)}"
+        return {
+            "submission": {"task_id": task_id, "submitted_answer": error_msg},
+            "log": {"Task ID": task_id, "Question": question, "Submitted Answer": error_msg}
+        }
+
+async def run_questions_async(agent, questions_data: List[Dict]) -> tuple:
+    """Process questions sequentially with minimal rate limiting"""
+    submissions = []
+    logs = []
+    
+    total = len(questions_data)
+    for idx, q in enumerate(questions_data):
+        print(f"Processing {idx+1}/{total}: {q['question'][:80]}...")
+        
+        # Add small delay between requests
+        if idx > 0:
+            await asyncio.sleep(RATE_LIMIT_DELAY)
+        
+        result = await process_question(agent, q["question"], q["task_id"])
+        submissions.append(result["submission"])
+        logs.append(result["log"])
+    
+    return submissions, logs
+
+
+async def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the Agent on them, submits all answers,
+    and displays the results.
+    """
+    space_id = os.getenv("SPACE_ID")
+
+    if profile:
+        username = f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
+
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Instantiate Agent
+    try:
+        agent = Agent(
+            model=get_model("LiteLLMModel", MODEL_ID),
+            tools=get_tools()
+        )
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(f"Agent code: {agent_code}")
+
+    # 2. Fetch Questions
+    print(f"Fetching questions from: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+            print("Fetched questions list is empty.")
+            return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
+        estimated_time = len(questions_data) * RATE_LIMIT_DELAY / 60
+        print(f"⏱️ Estimated time: {estimated_time:.1f} minutes")
+    except Exception as e:
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
+
+    # 3. Run Agent
+    print(f"Running agent on {len(questions_data)} questions...")
+    answers_payload, results_log = await run_questions_async(agent, questions_data)
+
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+
+    # 4. Prepare Submission 
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
+    print(f"Submitting {len(answers_payload)} answers for user '{username}'...")
+
+    # 5. Submit
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        final_status = (
+            f"✅ Submission Successful!\n\n"
+            f"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n\n"
+            f"Message: {result_data.get('message', 'No message received.')}\n\n"
+            f"Leaderboard: {api_url}/leaderboard"
+        )
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except Exception as e:
+        status_message = f"❌ Submission Failed: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+
+# --- Build Gradio Interface ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🤖 GAIA Agent Evaluation")
+    gr.Markdown(
+        f"""
+        **Instructions:**
+        1. Log in to your Hugging Face account using the button below
+        2. Click 'Run Evaluation & Submit' to test your agent
+        3. The agent will use web search and other tools to answer questions
+        
+        **Current Setup:**
+        - Model: Llama 3.3 70B (via Groq)
+        - Tools: Web search, Wikipedia, calculation, and more
+        - Rate Limiting: {RATE_LIMIT_DELAY}s between requests
+        
+        ⚠️ **Note:** Make sure you have set your GROQ_API_KEY in the Space secrets.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button("🚀 Run Evaluation & Submit", variant="primary")
+
+    status_output = gr.Textbox(label="📊 Status / Results", lines=8, interactive=False)
+    results_table = gr.DataFrame(label="📋 Questions and Answers", wrap=True, max_height=400)
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+if __name__ == "__main__":
+    print("\n" + "="*70)
+    print("🤖 GAIA Agent Starting")
+    print("="*70)
+    print(f"📝 Using Model: {MODEL_ID}")
+    
+    space_host = os.getenv("SPACE_HOST")
+    space_id = os.getenv("SPACE_ID")
+
+    if space_host:
+        print(f"✅ Runtime URL: https://{space_host}.hf.space")
+    if space_id:
+        print(f"✅ Repo URL: https://huggingface.co/spaces/{space_id}")
+    
+    print("="*70 + "\n")
+    demo.launch(debug=True, share=False)