Update app.py

app.py

@@ -14,36 +14,29 @@ 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("Initializing BERT-based QA agent...")
-        # Cargar el modelo BERT preentrenado en SQuAD para tareas de Pregunta y Respuesta
-        self.qa_pipeline = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")
+        print("Initializing T5-based QA agent with custom prompt...")
+        # Load the T5 model
+        self.qa_pipeline = pipeline("text2text-generation", model="t5-large")
     
     def __call__(self, question: str) -> str:
         """
-        Procesa la pregunta y devuelve una respuesta basada en el contexto proporcionado.
-        Si no se proporciona contexto, devuelve un mensaje de error.
+        Process the question using T5 with a detailed prompt and return an answer.
         """
-        prompt = """You are a general AI assistant. I will ask you a question based on the provided context. 
-Please provide the answer in a clear and concise manner."""
-        
-        # Crear un prompt dentro del contexto que estructure la tarea más explícitamente
+        # Create a more structured and detailed prompt
         prompt = f"""
-You are a general AI assistant. I will ask you a question based on the provided context. 
-Please provide the answer in a clear and concise manner.
-Question: {question}
-Context: {prompt}
-Answer:
-"""
+        You are an intelligent assistant. Please answer the following question as accurately as possible:
+        Question: {question}
+        Answer:
+        """
         
         try:
-            # Usar el pipeline para obtener la respuesta de la pregunta con el contexto
-            result = self.qa_pipeline(question=question, context=prompt)
-            answer = result["answer"]
+            # Use the T5 pipeline to generate an answer
+            result = self.qa_pipeline(prompt)
+            answer = result[0]["generated_text"].strip()
         except Exception as e:
-            print(f"Error durante QA: {e}")
+            print(f"Error during QA: {e}")
             answer = "Error processing question."
         
-        # Devuelve la respuesta final con el formato requerido
         return answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):