Update app.py

app.py

@@ -4,6 +4,13 @@ import requests
 import inspect
 import pandas as pd
 
+try:
+    from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+    SMOLAGENTS_AVAILABLE = True
+except ImportError:
+    print("Warning: smolagents not available, using fallback implementation")
+    SMOLAGENTS_AVAILABLE = False
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -11,13 +18,105 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
+    """
+    Simple agent using smolagents CodeAgent with DuckDuckGoSearchTool.
+    
+    Based on Unit 2 course content:
+    - Uses CodeAgent from smolagents (writes Python code to solve problems)
+    - Has access to DuckDuckGoSearchTool for web search
+    - Follows ReAct pattern automatically within CodeAgent
+    
+    How ReAct works in smolagents:
+    1. CodeAgent receives a question
+    2. INTERNAL LOOP (hidden from us):
+       - Think: LLM reasons about what to do next
+       - Act: LLM writes Python code (may call tools like DuckDuckGoSearchTool)
+       - Observe: LLM sees the code execution results
+       - Repeat: If not satisfied, continues the loop (up to max_steps)
+    3. Returns final answer when LLM decides it's complete
+    
+    The while loop is built into CodeAgent - we just call agent.run(question)!
+    """
+    
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("SimpleCodeAgent initialized.")
+        
+        if SMOLAGENTS_AVAILABLE:
+            try:
+                # Initialize the model (from Unit 2)
+                self.model = HfApiModel()
+                
+                # Create CodeAgent with DuckDuckGoSearchTool (core Unit 2 concept)
+                self.agent = CodeAgent(
+                    tools=[DuckDuckGoSearchTool()],
+                    model=self.model,
+                    additional_authorized_imports=[
+                        'math', 'statistics', 're',  # Basic computation
+                        'requests', 'json',           # Web requests and JSON
+                        'pandas', 'numpy',            # Data analysis  
+                        'zipfile', 'os',              # File processing
+                        'datetime', 'time'            # Date/time operations
+                    ],
+                    system_prompt="""You are an AI assistant that answers questions accurately and concisely.
+
+When answering questions:
+- Provide ONLY the direct answer requested
+- For numbers, give just the number (e.g., "42", not "The answer is 42")
+- For names/words, give just the name/word (e.g., "Paris", not "The capital is Paris")
+- For yes/no questions, answer just "Yes" or "No"
+- Do not include explanations unless specifically asked
+- Do not add prefixes like "The answer is" or "According to my search"
+
+You have access to web search via DuckDuckGoSearchTool when you need current information.
+You can write Python code to perform calculations or process data.
+
+Use the Think-Act-Observe pattern:
+1. Think about what you need to do
+2. Act by writing code or using tools
+3. Observe the results
+4. Continue until you have the final answer
+
+Always end with just the direct answer the question asks for."""
+                )
+                
+                self.available = True
+                print("✅ Smolagents CodeAgent initialized successfully")
+                
+            except Exception as e:
+                print(f"⚠️ Error initializing smolagents: {e}")
+                self.available = False
+        else:
+            self.available = False
+    
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        """
+        Main agent execution - just pass the question to CodeAgent.
+        The CodeAgent will Think-Act-Observe automatically and format the answer properly.
+        """
+        print(f"Agent processing: {question[:80]}...")
+        
+        if not self.available:
+            return self._fallback_response(question)
+        
+        try:
+            # Let the CodeAgent handle everything with ReAct pattern
+            # The LLM is smart enough to format answers properly for GAIA
+            result = self.agent.run(question)
+            
+            final_answer = str(result).strip()
+            print(f"Agent answer: {final_answer}")
+            return final_answer
+            
+        except Exception as e:
+            print(f"Agent error: {e}")
+            return "Unable to process question"
+    
+    def _fallback_response(self, question: str) -> str:
+        """
+        Fallback if smolagents is not available.
+        """
+        print("Smolagents not available - using basic fallback")
+        return "Smolagents required but not available"
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """