Update app.py

app.py

@@ -1,4 +1,5 @@
 import json
+import numpy as np
 import os
 import faiss
 from sentence_transformers import SentenceTransformer
@@ -30,9 +31,17 @@ def generate_advice(extracted_data):
     try:
         recommendations = []
         for item in extracted_data:
-            # Query FAISS for the closest match
+            # Prepare the query string
             query = f"{item['Component']} {item['Status']}"
+            
+            # Generate query embedding (ensure it's a valid 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.")
+
+            # Search the FAISS index for the closest match
             _, idx = index.search(query_embedding, 1)
             best_match = kb[idx[0][0]]
 
@@ -75,7 +84,7 @@ def generate_advice(extracted_data):
         return recommendations
 
     except Exception as e:
-        # Ensure Gradio JSON output is always valid
+        # Return structured error message
         return [{"error": str(e)}]
 
 # Gradio app with LLM integration