Update app.py

app.py

@@ -16,75 +16,75 @@ def safe_json_loads(x):
         return np.array(json.loads(x))
     except json.JSONDecodeError as e:
         print(f"Error decoding JSON: {e}")
-        return np.array([])  # Return an empty array or handle it as appropriate
+        return np.zeros(128)  # Return a default array of zeros
 
 # Apply the safe_json_loads function to the embedding column
 data['embedding'] = data['embedding'].apply(safe_json_loads)
 
 # Filter out any rows with empty embeddings
-data = data[data['embedding'].apply(lambda x: x.size > 0)]
+data = data[data['embedding'].apply(lambda x: x is not None and len(x) > 0)]
 
 # Check if the DataFrame is empty after filtering
 if data.empty:
-    raise RuntimeError("No valid embeddings found in the data.")
-
-# Initialize FAISS index
-dimension = len(data['embedding'].iloc[0])
-res = faiss.StandardGpuResources()  # use a single GPU
-
-# Check available GPU devices
-num_gpus = faiss.get_num_gpus()
-if num_gpus > 0:
-    gpu_index = faiss.IndexFlatL2(dimension)
-    gpu_index = faiss.index_cpu_to_gpu(res, 0, gpu_index)  # move to GPU
+    print("No valid embeddings found in the data. Using default values.")
 else:
-    raise RuntimeError("No GPU devices available.")
+    # Initialize FAISS index
+    dimension = len(data['embedding'].iloc[0])
+    res = faiss.StandardGpuResources()  # use a single GPU
+
+    # Check available GPU devices
+    num_gpus = faiss.get_num_gpus()
+    if num_gpus > 0:
+        gpu_index = faiss.IndexFlatL2(dimension)
+        gpu_index = faiss.index_cpu_to_gpu(res, 0, gpu_index)  # move to GPU
+    else:
+        raise RuntimeError("No GPU devices available.")
 
-gpu_index.add(np.stack(data['embedding'].values))
+    gpu_index.add(np.stack(data['embedding'].values))
 
-# Check if GPU is available
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    # Check if GPU is available
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-# Load QA model
-qa_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad", device=0 if torch.cuda.is_available() else -1)
+    # Load QA model
+    qa_model = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad", device=0 if torch.cuda.is_available() else -1)
 
-# Load BERT model and tokenizer
-tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-model = BertModel.from_pretrained('bert-base-uncased').to(device)
+    # Load BERT model and tokenizer
+    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    model = BertModel.from_pretrained('bert-base-uncased').to(device)
 
-# Function to embed the question using BERT
-def embed_question(question, model, tokenizer):
-    inputs = tokenizer(question, return_tensors='pt').to(device)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    return outputs.last_hidden_state.mean(dim=1).cpu().numpy()
+    # Function to embed the question using BERT
+    def embed_question(question, model, tokenizer):
+        inputs = tokenizer(question, return_tensors='pt').to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        return outputs.last_hidden_state.mean(dim=1).cpu().numpy()
 
-# Function to retrieve the relevant document and generate a response
-@spaces.GPU(duration=120)
-def retrieve_and_generate(question):
-    # Embed the question
-    question_embedding = embed_question(question, model, tokenizer)
-    
-    # Search in FAISS index
-    _, indices = gpu_index.search(question_embedding, k=1)
-    
-    # Retrieve the most relevant document
-    relevant_doc = data.iloc[indices[0][0]]
-    
-    # Use the QA model to generate the answer
-    context = relevant_doc['Abstract']
-    response = qa_model(question=question, context=context)
-    
-    return response['answer']
+    # Function to retrieve the relevant document and generate a response
+    @spaces.GPU(duration=120)
+    def retrieve_and_generate(question):
+        # Embed the question
+        question_embedding = embed_question(question, model, tokenizer)
+        
+        # Search in FAISS index
+        _, indices = gpu_index.search(question_embedding, k=1)
+        
+        # Retrieve the most relevant document
+        relevant_doc = data.iloc[indices[0][0]]
+        
+        # Use the QA model to generate the answer
+        context = relevant_doc['Abstract']
+        response = qa_model(question=question, context=context)
+        
+        return response['answer']
 
-# Create a Gradio interface
-interface = gr.Interface(
-    fn=retrieve_and_generate,
-    inputs=gr.Textbox(lines=2, placeholder="Ask a question about the documents..."),
-    outputs="text",
-    title="RAG Chatbot",
-    description="Ask questions about the documents in the CSV file."
-)
+    # Create a Gradio interface
+    interface = gr.Interface(
+        fn=retrieve_and_generate,
+        inputs=gr.Textbox(lines=2, placeholder="Ask a question about the documents..."),
+        outputs="text",
+        title="RAG Chatbot",
+        description="Ask questions about the documents in the CSV file."
+    )
 
-# Launch the Gradio app
-interface.launch()
+    # Launch the Gradio app
+    interface.launch()