Update app.py

app.py

@@ -36,27 +36,18 @@ def generate_advice(extracted_data):
             query = f"{item['Component']} {item['Status']}"
             print(f"Processing query: {query}")  # Debug print
 
-            # Generate query embedding and force it into 2D float32 numpy array
+            # Generate query embedding and ensure it’s a 2D numpy array
             query_embedding = embedding_model.encode([query])
-            if not isinstance(query_embedding, np.ndarray):
-                query_embedding = np.array(query_embedding, dtype="float32")
+            query_embedding = np.array(query_embedding, dtype="float32").reshape(1, -1)
 
-            query_embedding = query_embedding.reshape(1, -1).astype("float32")
-
-            # Debugging the embedding
-            print(f"Query Embedding Shape: {query_embedding.shape}, FAISS Index Dim: {index.d}")
-
-            # Validate shape compatibility
-            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
+            # Search for the closest match in FAISS
             _, idx = index.search(query_embedding, 1)
-            print(f"FAISS Search Result: {idx}")  # Debug print
+            print(f"FAISS Index: {idx}, Best Match Raw: {kb[idx[0][0]]}")  # Debug print
 
+            # Retrieve the closest match and validate
             best_match = kb[idx[0][0]]
+            if not isinstance(best_match, dict):
+                raise ValueError(f"Best match retrieved is not a dictionary: {best_match}")
 
             # Prepare the LLM prompt
             role = "Medical expert providing advice based on lab results."
@@ -82,7 +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,