Update app.py

app.py

@@ -4,6 +4,47 @@ import requests
 import inspect
 import pandas as pd
 
+
+# (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:
+    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]}...")
+        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):
@@ -183,36 +224,3 @@ if __name__ == "__main__":
 
 
 
-# (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:
-    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]}...")
-        response = self.query_engine.query(question)
-        print(f"Agent returning response: {response}")
-        return str(response)