Update app.py

app.py

@@ -13,26 +13,28 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
-    print("Initializing LlamaIndex-based agent...")
 
-    # Imposta l'LLM (puoi usare anche altri modelli via HuggingFace o OpenRouter)
-    self.llm = HfApiModel()
-
-    #OpenAI(model="gpt-3.5-turbo", temperature=0)
-
-    # Crea un ServiceContext con il tuo LLM
-    self.service_context = ServiceContext.from_defaults(llm=self.llm)
-
-    # Carica i documenti dalla directory "data/"
-    self.documents = SimpleDirectoryReader("data").load_data()
-
-    # Crea un indice vettoriale
-    self.index = VectorStoreIndex.from_documents(
-        self.documents, service_context=self.service_context
-    )
-
-    # Crea il query engine
-    self.query_engine = self.index.as_query_engine()
+    def __init__(self):
+        print("Initializing LlamaIndex-based agent...")
+    
+        # Imposta l'LLM (puoi usare anche altri modelli via HuggingFace o OpenRouter)
+        self.llm = HfApiModel()
+    
+        #OpenAI(model="gpt-3.5-turbo", temperature=0)
+    
+        # Crea un ServiceContext con il tuo LLM
+        self.service_context = ServiceContext.from_defaults(llm=self.llm)
+    
+        # Carica i documenti dalla directory "data/"
+        self.documents = SimpleDirectoryReader("data").load_data()
+    
+        # Crea un indice vettoriale
+        self.index = VectorStoreIndex.from_documents(
+            self.documents, service_context=self.service_context
+        )
+    
+        # Crea il query engine
+        self.query_engine = self.index.as_query_engine()
 
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")