Update app.py

app.py

@@ -1,10 +1,10 @@
 import os
 import gradio as gr
 import requests
-import inspect
+import inspect 
 import pandas as pd
+from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
 
-# (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
@@ -12,12 +12,51 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Inizializzazione del VERO Agente AI...")
+        
+        # 1. Definisci il modello (usiamo Qwen su HF)
+        self.model = HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct")
+        
+        # 2. Fornisci all'agente gli strumenti per cercare su internet
+        self.tools = [DuckDuckGoSearchTool()]
+        
+        # 3. Crea l'agente
+        self.agent = CodeAgent(
+            tools=self.tools,
+            model=self.model,
+            max_steps=10,
+            additional_authorized_imports=["requests", "bs4", "json", "time"]
+        )
+        
+        # 4. IL TRUCCO PER L'EXACT MATCH: Istruzioni severissime
+        self.prompt_template = """
+        You must answer the following question.
+        
+        CRITICAL RULE (EXACT MATCH): 
+        You must output ONLY the exact requested answer. 
+        Absolutely NO introductory phrases, no "The answer is...", no "FINAL ANSWER:". 
+        Just return the raw data, word, or number.
+        
+        Question to solve: {question}
+        """
+
     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
+        try:
+            # Combina la domanda con le nostre istruzioni severe
+            formatted_prompt = self.prompt_template.format(question=question)
+            
+            # Fai ragionare e agire il tuo agente!
+            answer = self.agent.run(formatted_prompt)
+            
+            # Pulisce la stringa da eventuali spazi extra
+            final_answer = str(answer).strip() 
+            print(f"L'agente ha trovato la risposta: {final_answer}")
+            return final_answer
+        except Exception as e:
+            print(f"Errore durante l'esecuzione dell'agente: {e}")
+            return "Error"
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -163,7 +202,7 @@ with gr.Blocks() as demo:
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
+    
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(