Update app.py

app.py

@@ -1,8 +1,273 @@
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+import os
+import gradio as gr
+import requests
+import pandas as pd
+from smolagents import CodeAgent, DuckDuckGoSearchTool, LiteLLMModel
 
-# 1. Define the tool (the agent's "eyes")
-search_tool = DuckDuckGoSearchTool()
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# 2. Define the model (the agent's "brain")
-# We use a standard open model so you don't need a paid API key
-model = HfApiModel()
+# --- Agent Definition ---
+class BasicAgent:
+    def __init__(self):
+        print("Initializing smolagents CodeAgent...")
+        try:
+            # Create model - using FREE Llama model
+            self.model = LiteLLMModel(
+                model_id="huggingface/meta-llama/Llama-3.2-3B-Instruct"
+            )
+            
+            # Create search tool
+            self.search_tool = DuckDuckGoSearchTool()
+            
+            # Create agent with tools
+            self.agent = CodeAgent(
+                tools=[self.search_tool],
+                model=self.model,
+                additional_authorized_imports=[
+                    "requests", 
+                    "pandas", 
+                    "numpy", 
+                    "openpyxl", 
+                    "xlrd"
+                ]
+            )
+            print("✅ Agent initialized successfully!")
+        except Exception as e:
+            print(f"❌ Error initializing agent: {e}")
+            raise e
+    
+    def __call__(self, question: str) -> str:
+        """Run agent on a question and return the answer"""
+        print(f"📝 Question: {question[:80]}...")
+        try:
+            # Run the agent
+            answer = self.agent.run(question)
+            answer_str = str(answer)
+            print(f"✅ Answer: {answer_str[:80]}...")
+            return answer_str
+        except Exception as e:
+            error_msg = f"Error: {str(e)}"
+            print(f"❌ {error_msg}")
+            return error_msg
+
+
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Fetches questions, runs agent, submits answers, returns results.
+    """
+    # Get Space ID
+    space_id = os.getenv("SPACE_ID")
+
+    # Check if user is logged in
+    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 above.", None
+
+    # API endpoints
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+
+    # 1. Initialize Agent
+    print("\n" + "="*60)
+    print("🤖 INITIALIZING AGENT")
+    print("="*60)
+    try:
+        agent = BasicAgent()
+    except Exception as e:
+        error_msg = f"Error initializing agent: {e}"
+        print(f"❌ {error_msg}")
+        return error_msg, None
+    
+    # Code URL for submission
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(f"📂 Code URL: {agent_code}")
+
+    # 2. Fetch Questions
+    print("\n" + "="*60)
+    print("📥 FETCHING QUESTIONS")
+    print("="*60)
+    print(f"URL: {questions_url}")
+    try:
+        response = requests.get(questions_url, timeout=15)
+        response.raise_for_status()
+        questions_data = response.json()
+        
+        if not questions_data:
+            return "No questions received from server.", None
+            
+        print(f"✅ Fetched {len(questions_data)} questions")
+    except Exception as e:
+        error_msg = f"Error fetching questions: {e}"
+        print(f"❌ {error_msg}")
+        return error_msg, None
+
+    # 3. Run Agent on Questions
+    print("\n" + "="*60)
+    print("🚀 RUNNING AGENT ON QUESTIONS")
+    print("="*60)
+    
+    results_log = []
+    answers_payload = []
+    
+    for idx, item in enumerate(questions_data, 1):
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        
+        if not task_id or question_text is None:
+            print(f"⚠️ Skipping invalid question")
+            continue
+        
+        print(f"\n[{idx}/{len(questions_data)}] Task ID: {task_id}")
+        
+        try:
+            # Run agent
+            submitted_answer = agent(question_text)
+            
+            # Store answer
+            answers_payload.append({
+                "task_id": task_id,
+                "submitted_answer": submitted_answer
+            })
+            
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": submitted_answer
+            })
+            
+        except Exception as e:
+            error_answer = f"AGENT ERROR: {e}"
+            print(f"❌ Error: {e}")
+            
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": error_answer
+            })
+
+    if not answers_payload:
+        return "Agent did not produce any answers.", pd.DataFrame(results_log)
+
+    # 4. Submit Answers
+    print("\n" + "="*60)
+    print("📤 SUBMITTING ANSWERS")
+    print("="*60)
+    print(f"Submitting {len(answers_payload)} answers for user: {username}")
+    
+    submission_data = {
+        "username": username.strip(),
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
+    
+    try:
+        response = requests.post(submit_url, json=submission_data, timeout=60)
+        response.raise_for_status()
+        result_data = response.json()
+        
+        # Format success message
+        final_status = (
+            f"✅ Submission Successful!\n\n"
+            f"User: {result_data.get('username')}\n"
+            f"Score: {result_data.get('score', 'N/A')}%\n"
+            f"Correct: {result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')}\n"
+            f"Message: {result_data.get('message', 'No message')}"
+        )
+        
+        print("="*60)
+        print(final_status)
+        print("="*60)
+        
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+        
+    except Exception as e:
+        error_msg = f"❌ Submission Failed: {e}"
+        print(error_msg)
+        results_df = pd.DataFrame(results_log)
+        return error_msg, results_df
+
+
+# --- Gradio Interface ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🤖 smolagents Exam Runner")
+    gr.Markdown(
+        """
+        ### Instructions:
+        1. **Log in** with your Hugging Face account (button below)
+        2. **Click** "Run Evaluation & Submit All Answers"
+        3. **Wait** - This takes several minutes as the agent answers each question
+        4. **View** your score and answers
+        
+        ---
+        
+        ⚠️ **Note:** The agent uses web search and code execution to answer questions.
+        This may take 5-10 minutes to complete all questions.
+        """
+    )
+
+    gr.LoginButton()
+
+    run_button = gr.Button(
+        "🚀 Run Evaluation & Submit All Answers",
+        variant="primary",
+        size="lg"
+    )
+
+    status_output = gr.Textbox(
+        label="📊 Status & Results",
+        lines=10,
+        interactive=False
+    )
+    
+    results_table = gr.DataFrame(
+        label="📝 Questions and Agent Answers",
+        wrap=True
+    )
+
+    run_button.click(
+        fn=run_and_submit_all,
+        outputs=[status_output, results_table]
+    )
+
+    gr.Markdown(
+        """
+        ---
+        **Tips:**
+        - The agent searches the web for answers
+        - Some questions require data analysis or file processing
+        - Answers are submitted automatically after completion
+        """
+    )
+
+
+if __name__ == "__main__":
+    print("\n" + "="*70)
+    print(" " * 20 + "🚀 SMOLAGENTS EXAM RUNNER")
+    print("="*70)
+    
+    # Check environment variables
+    space_host = os.getenv("SPACE_HOST")
+    space_id = os.getenv("SPACE_ID")
+
+    if space_host:
+        print(f"✅ SPACE_HOST: {space_host}")
+        print(f"   Runtime URL: https://{space_host}.hf.space")
+    else:
+        print("ℹ️  SPACE_HOST not found (running locally?)")
+
+    if space_id:
+        print(f"✅ SPACE_ID: {space_id}")
+        print(f"   Repo: https://huggingface.co/spaces/{space_id}")
+    else:
+        print("ℹ️  SPACE_ID not found (running locally?)")
+
+    print("="*70 + "\n")
+    print("🎬 Launching Gradio Interface...\n")
+    
+    demo.launch(debug=True, share=False)