Update app.py

app.py

@@ -30,18 +30,22 @@ llm = AutoModelForCausalLM.from_pretrained(llama_model_name, token=API_TOKEN)
 def generate_advice(extracted_data):
     try:
         recommendations = []
+
         for item in extracted_data:
             # Prepare the query string
             query = f"{item['Component']} {item['Status']}"
             
-            # Generate query embedding (ensure it's a valid numpy array)
+            # Generate query embedding and ensure it’s a 2D numpy array
             query_embedding = embedding_model.encode([query])
-            query_embedding = np.array(query_embedding).astype('float32')  # Explicit cast
-            
-            if query_embedding.shape != (1, index.d):  # FAISS index shape check
-                raise ValueError("Query embedding shape mismatch. Check input to the embedding model.")
+            query_embedding = np.array(query_embedding, dtype="float32").reshape(1, -1)
 
-            # Search the FAISS index for the closest match
+            # Check query embedding shape against FAISS index
+            if query_embedding.shape[1] != index.d:
+                raise ValueError(
+                    f"Embedding dimension mismatch: Query ({query_embedding.shape[1]}), Index ({index.d})"
+                )
+
+            # Search for the closest match in the FAISS index
             _, idx = index.search(query_embedding, 1)
             best_match = kb[idx[0][0]]
 
@@ -69,7 +73,7 @@ def generate_advice(extracted_data):
                 return_tensors="pt",
             )
 
-            # Generate response
+            # Generate response using LLaMA
             output = llm.generate(
                 input_ids=input_text_with_your_role,
                 max_length=150,
@@ -77,15 +81,16 @@ def generate_advice(extracted_data):
             )
             advice = tokenizer.decode(output[0], skip_special_tokens=True).strip()
 
+            # Append results
             recommendations.append({
                 "Component": item["Component"],
                 "Advice": advice
             })
+
         return recommendations
 
     except Exception as e:
-        # Return structured error message
-        return [{"error": str(e)}]
+        return [{"error": f"Exception occurred: {str(e)}"}]
 
 # Gradio app with LLM integration
 import gradio as gr