Update app.py

app.py

@@ -213,14 +213,38 @@ objection_response_pairs = load_objection_responses(r"C:\Users\bhagy\OneDrive\De
 objections = list(objection_response_pairs.keys())
 objection_embeddings = sentence_model.encode(objections)
 objection_embeddings = np.array(objection_embeddings, dtype="float32")
+print(f"Shape of objection_embeddings: {objection_embeddings.shape}")
+
+# Assuming you know the expected dimension of the embeddings (e.g., 768)
+expected_dim = 768  # Example value for sentence embeddings (replace with the actual dimension)
+
+# Check if the embeddings dimensionality matches the expected dimension
+if objection_embeddings.shape[1] != expected_dim:
+    raise ValueError(f"Dimensionality of embeddings {objection_embeddings.shape[1]} does not match expected dimension {expected_dim}.")
+
+# If the shape is (1, d) (e.g., a single embedding), reshape it to (1, d)
 if len(objection_embeddings.shape) == 1:
     objection_embeddings = objection_embeddings.reshape(1, -1)  # Reshape for a single embedding
 elif len(objection_embeddings.shape) == 2:
-    pass  
+    pass  # The shape is already in the expected form (num_samples, dim)
 else:
-    raise ValueError("Unexpected shape for objection embeddings:", objection_embeddings.shape)
+    raise ValueError(f"Unexpected shape for objection embeddings: {objection_embeddings.shape}")
+
+# Create Faiss index with the correct dimensionality (make sure this matches the embedding size)
+faiss_index = faiss.IndexFlatL2(expected_dim)
+
+# Now add the embeddings to the Faiss index
 faiss_index.add(objection_embeddings)
 
+print(f"Successfully added {objection_embeddings.shape[0]} embeddings to the Faiss index.")
+# if len(objection_embeddings.shape) == 1:
+#     objection_embeddings = objection_embeddings.reshape(1, -1)  # Reshape for a single embedding
+# elif len(objection_embeddings.shape) == 2:
+#     pass  
+# else:
+#     raise ValueError("Unexpected shape for objection embeddings:", objection_embeddings.shape)
+# faiss_index.add(objection_embeddings)
+
 def find_closest_objection(query):
     query_embedding = sentence_model.encode([query])
     distances, indices = faiss_index.search(np.array(query_embedding, dtype="float32"), 1)