Update app.py

app.py

@@ -7,56 +7,46 @@ from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, OpenAIServer
 from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
 from llama_index.llms import OpenAI
 from llama_index.settings import Settings
+import os
 
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
-
     def __init__(self):
         print("Initializing LlamaIndex-based agent...")
-    
-        # Imposta l'LLM (puoi usare anche altri modelli via HuggingFace o OpenRouter)
-        self.llm = OpenAI(
+
+        # Leggi la chiave OpenAI dall'ambiente
+        openai_api_key = os.getenv("OPENAI_API_KEY")
+        if not openai_api_key:
+            raise ValueError("OPENAI_API_KEY not set!")
+
+        # Crea un'istanza di OpenAI
+        llm = OpenAI(
             model="gpt-4o-mini",
             temperature=0,
-            api_key=os.getenv("OPENAI_API_KEY")
+            api_key=openai_api_key
         )
-    
-        #OpenAI(model="gpt-3.5-turbo", temperature=0)
-    
+
         # Imposta le impostazioni tramite Settings
         settings = Settings(
             llm=llm
         )
-    
-        # Carica i documenti dalla directory "data/"
+
+        # Carica i documenti dalla cartella '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
+
+        # Crea l'indice con la configurazione
+        self.index = VectorStoreIndex.from_documents(self.documents, settings=settings)
+
+        # Prepara il query engine
         self.query_engine = self.index.as_query_engine()
+        print("Agent ready.")
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        print(f"Received question: {question[:50]}...")
         response = self.query_engine.query(question)
-        print(f"Agent returning response: {response}")
         return str(response)
 
 
 
-
-
-
-
 ##Roba per la valutazione        
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):