Update app.py

app.py

@@ -2,62 +2,55 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
+from smolagents import CodeAgent, HfApiModel, DuckDuckGoSearchTool, VisitWebpageTool
 
 # --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"  # ← FIXED: No space!
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-class BasicAgent:
+# --- Intelligent Agent with Tools ---
+class IntelligentAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Initializing IntelligentAgent with tools...")
+        try:
+            # Initialize the model (uses Qwen model on HF Inference)
+            model = HfApiModel()
+            
+            # Initialize tools for web search and browsing
+            search_tool = DuckDuckGoSearchTool()
+            web_tool = VisitWebpageTool()
+            
+            # Create CodeAgent with tools
+            self.agent = CodeAgent(
+                tools=[search_tool, web_tool],
+                model=model,
+                max_steps=10,
+                verbosity_level=1
+            )
+            print("Agent initialized successfully with search and web tools!")
+        except Exception as e:
+            print(f"Error initializing agent: {e}")
+            self.agent = None
 
-    def __call__(self, question:  str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        
-        # Basic intelligent response system
-        question_lower = question.lower()
-        
-        # Try to provide context-aware answers
-        if "capital" in question_lower:
-            if "france" in question_lower: 
-                return "Paris"
-            elif "germany" in question_lower:
-                return "Berlin"
-            elif "italy" in question_lower:
-                return "Rome"
-            elif "spain" in question_lower:
-                return "Madrid"
-            elif "japan" in question_lower:
-                return "Tokyo"
-        
-        if "who" in question_lower and "ceo" in question_lower: 
-            if "tesla" in question_lower or "spacex" in question_lower:
-                return "Elon Musk"
-            elif "apple" in question_lower:
-                return "Tim Cook"
-            elif "microsoft" in question_lower:
-                return "Satya Nadella"
-        
-        if "what year" in question_lower or "when" in question_lower:
-            if "world war 2" in question_lower or "wwii" in question_lower:
-                return "1939-1945"
-            elif "world war 1" in question_lower or "wwi" in question_lower:
-                return "1914-1918"
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question:  {question[:100]}...")
         
-        if "how many" in question_lower:
-            if "planets" in question_lower: 
-                return "8"
-            elif "continents" in question_lower:
-                return "7"
+        if self.agent is None:
+            return "Agent initialization failed."
         
-        # Default response
-        answer = "I don't have enough information to answer this question accurately."
-        print(f"Agent returning answer: {answer}")
-        return answer
+        try:
+            # Run the agent with the question
+            result = self.agent. run(question)
+            answer = str(result)
+            print(f"Agent answer: {answer[:200]}...")
+            return answer
+        except Exception as e: 
+            print(f"Error running agent: {e}")
+            # Fallback:  try to give a reasonable response
+            return f"I encountered an error while processing this question. Error: {str(e)}"
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them, submits all answers, and displays the results.
+    Fetches all questions, runs the IntelligentAgent on them, submits all answers, and displays the results. 
     """
     space_id = os.getenv("SPACE_ID")
     
@@ -74,132 +67,167 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     submit_url = f"{api_url}/submit"
 
     # 1. Instantiate Agent
-    try: 
-        agent = BasicAgent()
+    print("Creating agent instance...")
+    try:
+        agent = IntelligentAgent()
+        if agent. agent is None:
+            return "❌ Failed to initialize agent. Please check logs.", None
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
 
     # Build agent code URL
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(f"Agent code: {agent_code}")
+    print(f"Agent code:  {agent_code}")
 
     # 2. Fetch Questions
-    print(f"Fetching questions from:  {questions_url}")
+    print(f"Fetching questions from: {questions_url}")
     try:
-        response = requests.get(questions_url, timeout=15)
+        response = requests.get(questions_url, timeout=30)
         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.")
-    except requests. exceptions.RequestException as e:
+        print(f"✅ Fetched {len(questions_data)} questions.")
+    except requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
-    except requests. exceptions.JSONDecodeError as e:
-        print(f"Error decoding JSON response from questions endpoint:  {e}")
+    except requests.exceptions.JSONDecodeError as e:
+        print(f"Error decoding JSON response from questions endpoint: {e}")
         print(f"Response text: {response.text[: 500]}")
         return f"Error decoding server response for questions:  {e}", None
     except Exception as e:
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3. Run your Agent
+    # 3. Run your Agent on each question
     results_log = []
     answers_payload = []
-    print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data: 
+    print(f"\n🤖 Running agent on {len(questions_data)} questions...")
+    print("=" * 60)
+    
+    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 item with missing task_id or question: {item}")
+        
+        if not task_id or question_text is None:
+            print(f"⚠️ Skipping item with missing task_id or question:  {item}")
             continue
 
+        print(f"\n[{idx}/{len(questions_data)}] Processing task:  {task_id}")
+        print(f"Question: {question_text[: 100]}...")
+        
         try:
             answer = agent(question_text)
-            # Use "submitted_answer" as required by API
-            answers_payload. append({
+            answers_payload.append({
                 "task_id": task_id,
                 "submitted_answer": answer
             })
-            results_log.append((task_id, question_text[: 50], answer[: 100]))
+            results_log.append((task_id, question_text[: 50], answer[:100]))
+            print(f"✅ Answer generated:  {answer[:100]}...")
         except Exception as e:
-            print(f"Error running agent on question {task_id}: {e}")
+            print(f"❌ Error running agent on question {task_id}: {e}")
+            error_answer = "I apologize, but I encountered an error processing this question."
             answers_payload.append({
                 "task_id": task_id,
-                "submitted_answer": f"Error: {e}"
+                "submitted_answer": error_answer
             })
             results_log.append((task_id, question_text[: 50], f"Error: {e}"))
 
-    # 4. Submit answers with CORRECT payload format
-    print(f"Submitting {len(answers_payload)} answers to {submit_url}...")
+    print("\n" + "=" * 60)
+    print(f"✅ Completed processing all questions!")
+
+    # 4. Submit answers
+    print(f"\n📤 Submitting {len(answers_payload)} answers to {submit_url}...")
     try:
-        # Correct payload format
         payload = {
             "username": username,
             "answers": answers_payload,
             "agent_code": agent_code
         }
         
-        print(f"Payload structure:  username={username}, answers count={len(answers_payload)}, agent_code={agent_code}")
-        
-        submit_response = requests. post(
+        submit_response = requests.post(
             submit_url,
             json=payload,
-            timeout=30
+            timeout=60
         )
         submit_response.raise_for_status()
         submission_result = submit_response.json()
-        print(f"Submission successful:  {submission_result}")
+        print(f"✅ Submission successful:  {submission_result}")
     except requests.exceptions.HTTPError as e:
-        print(f"HTTP Error submitting answers: {e}")
-        print(f"Response text: {submit_response.text[: 500]}")
+        print(f"❌ HTTP Error submitting answers: {e}")
+        print(f"Response text: {submit_response.text[:500]}")
         return f"Error submitting answers: {e}\n\nResponse: {submit_response.text[:200]}", None
-    except Exception as e: 
-        print(f"Error submitting answers: {e}")
+    except Exception as e:
+        print(f"❌ Error submitting answers: {e}")
         return f"Error submitting answers: {e}", None
 
     # Display results
-    results_df = pd. DataFrame(results_log, columns=["task_id", "question", "answer"])
+    results_df = pd.DataFrame(results_log, columns=["task_id", "question", "answer"])
     
     # Extract score from submission result
     score = submission_result.get('score', 'N/A')
+    total_questions = len(answers_payload)
+    
     result_message = f"""
-## ✅ Submission Successful!
+## 🎉 Submission Successful!
+
+### 📊 Your Results:
+- **Score:** {score}
+- **Username:** {username}
+- **Questions Answered:** {total_questions}
 
-**Score:** {score}
+### 🏆 Score Breakdown:
+- ✅ **Pass Threshold:** 30%
+- 📈 **Your Score:** {score}
 
-**Username:** {username}
+{f"🎓 **Congratulations!  You passed Unit 4! **" if isinstance(score, (int, float)) and score >= 30 else "📚 Keep improving your agent to reach 30%! "}
 
-**Questions Answered:** {len(answers_payload)}
+### 🔗 Links:
+- [View Your Agent Code]({agent_code})
+- [Course Unit 4](https://huggingface.co/learn/agents-course/en/unit4/hands-on)
 
-🔗 [View Your Agent Code]({agent_code})
+---
+*Agent powered by smolagents with DuckDuckGo Search & Web Browsing*
     """
     
     return result_message, results_df
 
 # --- Gradio UI Setup ---
-with gr.Blocks() as demo:
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
-    # 🤖 Agent Final Assignment - Unit 4
+    # 🤖 Intelligent Agent - Unit 4 Final Assignment
+    
+    This agent uses **smolagents** with: 
+    - 🔍 **DuckDuckGo Search** - for finding information on the web
+    - 🌐 **Web Page Visitor** - for reading webpage content
+    - 🧠 **Code Agent** - for reasoning and executing Python code
     
-    **Instructions:**
-    1. Click the "Sign in with Hugging Face" button below
-    2. After logging in, click "Run Evaluation & Submit All Answers"
-    3. Wait for your score! 
+    ## 📋 Instructions:
+    1. ✅ Click "Sign in with Hugging Face" below
+    2. 🚀 Click "Run Evaluation & Submit All Answers"
+    3. ⏳ Wait for your score (this may take a few minutes)
+    
+    **Target:** Get 30% or higher to pass Unit 4!
     """)
     
     with gr.Row():
         gr.LoginButton()
     
     with gr.Row():
-        submit_button = gr.Button("🚀 Run Evaluation & Submit All Answers", variant="primary", size="lg")
+        submit_button = gr.Button(
+            "🚀 Run Evaluation & Submit All Answers", 
+            variant="primary", 
+            size="lg"
+        )
     
-    output_text = gr.Markdown()
-    output_table = gr.Dataframe(label="Results")
+    with gr.Row():
+        output_text = gr.Markdown()
+    
+    with gr.Row():
+        output_table = gr. Dataframe(label="📝 Detailed Results", wrap=True)
 
-    # The profile is automatically injected when using gr.LoginButton()
     submit_button.click(
         run_and_submit_all,
         inputs=None,