Update app.py

app.py

@@ -4,20 +4,7 @@ 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:
-    def __init__(self):
-        print("BasicAgent initialized.")
-    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
+##Roba per la valutazione        
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -194,70 +181,38 @@ if __name__ == "__main__":
     demo.launch(debug=True, share=False)
 
 
-'''
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
 
 
-if __name__ == "__main__":
-    demo.launch()
+# (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)