Update app.py

app.py

@@ -57,9 +57,12 @@ model = BertModel.from_pretrained('bert-base-uncased').to(device)
 def embed_question(question, model, tokenizer):
     try:
         inputs = tokenizer(question, return_tensors='pt').to(device)
+        logging.debug(f"Tokenized inputs: {inputs}")
         with torch.no_grad():
             outputs = model(**inputs)
-        return outputs.last_hidden_state.mean(dim=1).cpu().numpy().astype(np.float32)
+        embedding = outputs.last_hidden_state.mean(dim=1).cpu().numpy().astype(np.float32)
+        logging.debug(f"Question embedding: {embedding}")
+        return embedding
     except Exception as e:
         logging.error(f"Error embedding question: {e}")
         raise
@@ -71,22 +74,32 @@ def retrieve_and_generate(question):
     try:
         # Embed the question
         question_embedding = embed_question(question, model, tokenizer)
-        logging.debug(f"Question embedding: {question_embedding}")
 
         # Search in FAISS index
-        _, indices = gpu_index.search(question_embedding, k=1)
-        logging.debug(f"Indices found: {indices}")
+        try:
+            _, indices = gpu_index.search(question_embedding, k=1)
+            logging.debug(f"Indices found: {indices}")
+        except Exception as e:
+            logging.error(f"Error during FAISS search: {e}")
+            return "An error occurred during search. Please try again."
 
         # Retrieve the most relevant document
-        relevant_doc = data.iloc[indices[0][0]]
-        logging.debug(f"Relevant document: {relevant_doc}")
+        try:
+            relevant_doc = data.iloc[indices[0][0]]
+            logging.debug(f"Relevant document: {relevant_doc}")
+        except Exception as e:
+            logging.error(f"Error retrieving document: {e}")
+            return "An error occurred while retrieving the document. Please try again."
 
         # Use the QA model to generate the answer
-        context = relevant_doc['Abstract']
-        response = qa_model(question=question, context=context)
-        logging.debug(f"Response: {response}")
-
-        return response['answer']
+        try:
+            context = relevant_doc['Abstract']
+            response = qa_model(question=question, context=context)
+            logging.debug(f"Response: {response}")
+            return response['answer']
+        except Exception as e:
+            logging.error(f"Error generating answer: {e}")
+            return "An error occurred while generating the answer. Please try again."
     except Exception as e:
         logging.error(f"Error during retrieval and generation: {e}")
         return "An error occurred. Please try again."