Update app.py

app.py

@@ -2,66 +2,147 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-from smolagents import CodeAgent, DuckDuckGoSearchTool, VisitWebpageTool
-from smolagents.models import HfEngine
 
 # --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring. hf.space"
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Intelligent Agent with Tools ---
+# --- Intelligent Agent with Web Search ---
 class IntelligentAgent:
     def __init__(self):
-        print("Initializing IntelligentAgent with tools...")
+        print("Initializing IntelligentAgent...")
+        self.search_tool = self._create_search_function()
+        
+    def _create_search_function(self):
+        """Create a simple DuckDuckGo search function"""
+        try:
+            from duckduckgo_search import DDGS
+            return DDGS()
+        except: 
+            return None
+    
+    def search_web(self, query: str, max_results: int = 5) -> str:
+        """Search the web using DuckDuckGo"""
+        if self.search_tool is None:
+            return "Search unavailable"
+        
         try:
-            # Initialize the model engine
-            model = HfEngine(model_id="Qwen/Qwen2.5-Coder-32B-Instruct")
+            results = list(self.search_tool. text(query, max_results=max_results))
+            if not results:
+                return "No results found"
             
-            # Initialize tools for web search and browsing
-            search_tool = DuckDuckGoSearchTool()
-            web_tool = VisitWebpageTool()
+            # Format results
+            formatted = []
+            for i, r in enumerate(results[: max_results], 1):
+                formatted.append(f"{i}. {r. get('title', 'N/A')}: {r.get('body', 'N/A')}")
             
-            # 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!")
+            return "\n\n".join(formatted)
         except Exception as e:
-            print(f"Error initializing agent:  {e}")
-            self.agent = None
+            print(f"Search error: {e}")
+            return "Search failed"
 
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question: {question[:100]}...")
+    def __call__(self, question:  str) -> str:
+        print(f"Processing question: {question[:100]}...")
         
-        if self. agent is None:
-            return "Agent initialization failed."
+        # For questions that likely need web search
+        if any(keyword in question.lower() for keyword in ['current', 'latest', 'recent', 'today', 'now', '2024', '2025', '2026']):
+            print("Searching web for current information...")
+            search_results = self.search_web(question, max_results=3)
+            
+            # Try to extract relevant answer from search results
+            if search_results and search_results != "No results found":
+                # Return first meaningful snippet
+                lines = search_results.split('\n')
+                for line in lines:
+                    if len(line) > 20:
+                        return line[:500]
         
-        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."
+        # Basic knowledge-based responses
+        question_lower = question.lower()
+        
+        # Geography
+        if "capital" in question_lower: 
+            capitals = {
+                "france": "Paris", "germany": "Berlin", "italy": "Rome",
+                "spain": "Madrid", "japan": "Tokyo", "china": "Beijing",
+                "india": "New Delhi", "brazil": "Brasília", "canada": "Ottawa",
+                "australia": "Canberra", "russia": "Moscow", "uk": "London",
+                "united kingdom": "London", "usa": "Washington, D.C.",
+                "united states": "Washington, D.C.", "mexico": "Mexico City"
+            }
+            for country, capital in capitals.items():
+                if country in question_lower:
+                    return capital
+        
+        # Companies and CEOs
+        if "ceo" in question_lower or "chief executive" in question_lower:
+            ceos = {
+                "tesla": "Elon Musk", "spacex": "Elon Musk", "apple": "Tim Cook",
+                "microsoft": "Satya Nadella", "google": "Sundar Pichai",
+                "amazon": "Andy Jassy", "meta": "Mark Zuckerberg",
+                "facebook": "Mark Zuckerberg", "nvidia": "Jensen Huang"
+            }
+            for company, ceo in ceos.items():
+                if company in question_lower:
+                    return ceo
+        
+        # Historical dates
+        if "when" in question_lower or "what year" in question_lower:
+            events = {
+                "world war 2": "1939-1945", "world war ii": "1939-1945", "wwii": "1939-1945",
+                "world war 1": "1914-1918", "world war i": "1914-1918", "wwi": "1914-1918",
+                "moon landing": "1969", "first moon landing": "1969",
+                "declared independence": "1776", "independence america": "1776"
+            }
+            for event, date in events.items():
+                if event in question_lower: 
+                    return date
+        
+        # Counting questions
+        if "how many" in question_lower: 
+            counts = {
+                "planets": "8", "continents": "7", "oceans": "5",
+                "days in a week": "7", "months": "12", "states in usa": "50",
+                "states in us": "50"
+            }
+            for thing, count in counts.items():
+                if thing in question_lower: 
+                    return count
+        
+        # Math questions
+        if any(op in question for op in ['+', '-', '*', '/', '×', '÷']):
+            try:
+                # Simple eval for basic math (be careful with this in production!)
+                result = eval(question. replace('×', '*').replace('÷', '/'))
+                return str(result)
+            except:
+                pass
+        
+        # Try web search as fallback
+        print("Using web search as fallback...")
+        search_results = self.search_web(question, max_results=3)
+        if search_results and "No results found" not in search_results:
+            # Extract first meaningful content
+            lines = search_results.split('\n')
+            for line in lines:
+                if len(line. strip()) > 30:
+                    # Return cleaned up snippet
+                    return line. strip()[:500]
+        
+        # Final fallback
+        return "I don't have enough information to answer this question accurately."
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the IntelligentAgent 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")
     
-    # Check if user is logged in
     if profile is None:
         print("User not logged in.")
         return "❌ Please login to Hugging Face using the button above.", None
     
     username = profile.username
-    print(f"User logged in: {username}")
+    print(f"User logged in:  {username}")
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
@@ -71,53 +152,39 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     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}")
+        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}")
 
     # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
+    print(f"Fetching questions from:  {questions_url}")
     try:
         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
+            return "Fetched questions list is empty.", None
         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}")
-        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
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
 
-    # 3. Run your Agent on each question
+    # 3. Run Agent
     results_log = []
     answers_payload = []
     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}")
             continue
 
-        print(f"\n[{idx}/{len(questions_data)}] Processing task: {task_id}")
-        print(f"Question: {question_text[: 100]}...")
+        print(f"\n[{idx}/{len(questions_data)}] Task: {task_id}")
         
         try:
             answer = agent(question_text)
@@ -126,50 +193,34 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
                 "submitted_answer": answer
             })
             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}")
-            error_answer = "I apologize, but I encountered an error processing this question."
-            answers_payload. append({
+            print(f"✅ Answer:  {answer[:100]}")
+        except Exception as e: 
+            print(f"❌ Error:  {e}")
+            answers_payload.append({
                 "task_id": task_id,
-                "submitted_answer": error_answer
+                "submitted_answer": "Error processing question"
             })
-            results_log. append((task_id, question_text[:50], f"Error:  {e}"))
-
-    print("\n" + "=" * 60)
-    print(f"✅ Completed processing all questions!")
 
     # 4. Submit answers
-    print(f"\n📤 Submitting {len(answers_payload)} answers to {submit_url}...")
+    print(f"\n📤 Submitting {len(answers_payload)} answers...")
     try:
         payload = {
             "username": username,
             "answers": answers_payload,
-            "agent_code": agent_code
+            "agent_code":  agent_code
         }
         
-        submit_response = requests.post(
-            submit_url,
-            json=payload,
-            timeout=60
-        )
-        submit_response. raise_for_status()
-        submission_result = submit_response. json()
-        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]}")
-        return f"Error submitting answers: {e}\n\nResponse: {submit_response.text[:200]}", None
+        submit_response = requests. post(submit_url, json=payload, timeout=60)
+        submit_response.raise_for_status()
+        submission_result = submit_response.json()
+        print(f"✅ Submission successful:  {submission_result}")
     except Exception as e:
-        print(f"❌ Error submitting answers: {e}")
+        print(f"❌ Error submitting:  {e}")
         return f"Error submitting answers: {e}", None
 
     # Display results
     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!
@@ -177,62 +228,44 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 ### 📊 Your Results: 
 - **Score:** {score}
 - **Username:** {username}
-- **Questions Answered:** {total_questions}
+- **Questions Answered:** {len(answers_payload)}
 
 ### 🏆 Score Breakdown:
 - ✅ **Pass Threshold:** 30%
 - 📈 **Your Score:** {score}
 
-{f"🎓 **Congratulations!  You passed Unit 4! **" if isinstance(score, (int, float)) and score >= 30 else "📚 Keep improving your agent to reach 30%! "}
+{f"🎓 **Congratulations!  You passed Unit 4! **" if isinstance(score, (int, float)) and score >= 30 else "📚 Keep improving to reach 30%! "}
 
 ### 🔗 Links:
-- [View Your Agent Code]({agent_code})
+- [View Your Code]({agent_code})
 - [Course Unit 4](https://huggingface.co/learn/agents-course/en/unit4/hands-on)
-
----
-*Agent powered by smolagents with DuckDuckGo Search & Web Browsing*
     """
     
     return result_message, results_df
 
-# --- Gradio UI Setup ---
+# --- Gradio UI ---
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
+    gr. Markdown("""
     # 🤖 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
+    This agent uses: 
+    - 🔍 **DuckDuckGo Search** for web information
+    - 🧠 **Knowledge Base** for common questions
+    - 🎯 **Smart fallbacks** for better coverage
     
     ## 📋 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)
+    1. ✅ Sign in with Hugging Face
+    2. 🚀 Click "Run Evaluation & Submit"
+    3. ⏳ Wait for your score
     
-    **Target:** Get 30% or higher to pass Unit 4! 
+    **Target:** 30% or higher to pass! 
     """)
     
-    with gr.Row():
-        gr.LoginButton()
-    
-    with gr.Row():
-        submit_button = gr.Button(
-            "🚀 Run Evaluation & Submit All Answers",
-            variant="primary",
-            size="lg"
-        )
-    
-    with gr.Row():
-        output_text = gr. Markdown()
-    
-    with gr.Row():
-        output_table = gr.Dataframe(label="📝 Detailed Results", wrap=True)
-
-    submit_button.click(
-        run_and_submit_all,
-        inputs=None,
-        outputs=[output_text, output_table]
-    )
+    gr.LoginButton()
+    submit_button = gr.Button("🚀 Run Evaluation & Submit All Answers", variant="primary", size="lg")
+    output_text = gr.Markdown()
+    output_table = gr. Dataframe(label="📝 Results", wrap=True)
+
+    submit_button.click(run_and_submit_all, inputs=None, outputs=[output_text, output_table])
 
 demo.launch()