Update app.py

app.py

@@ -38,44 +38,40 @@ def generate_advice(extracted_data):
 
         for item in extracted_data:
             query = f"{item['Component']} {item['Status']}"
-            print(f"Query: {query}")  # Debugging step
+            print(f"Processing Query: {query}")  # Debugging step
 
-            # Generate query embedding
+            # Generate query embedding as float32
             query_embedding = embedding_model.encode([query])
             query_embedding = np.array(query_embedding, dtype="float32").reshape(1, -1)
 
-            # Validate embedding shape
-            if query_embedding.shape[1] != index.d:
-                raise ValueError(f"Embedding dimension mismatch: FAISS expects {index.d}, but got {query_embedding.shape[1]}")
-
             # Search FAISS for the closest match
             _, idx = index.search(query_embedding, 1)
             best_match = kb[idx[0][0]]
 
-            # LLM prompt
+            # Prepare LLM prompt
             role = "Medical expert providing advice based on lab results."
             prompt = f"""
             Lab Test: {item['Component']}
             Value: {item['Value']} {item['Units']}
             Status: {item['Status']}
-            
+
             Medical Guidelines: {best_match['Advice']}
-            
+
             Provide additional insights or recommendations.
             """
 
-            # Generate advice
+            # Generate advice with LLaMA
             message = [
                 {"role": "system", "content": role},
                 {"role": "user", "content": prompt},
             ]
 
             input_text = tokenizer.apply_chat_template(
-                message, tokenize=False, add_generation_prompt=True, return_tensors="pt"
+                message, tokenize=True, add_generation_prompt=True, return_tensors="pt"
             )
 
             output = llm.generate(
-                input_ids=input_text,
+                input_ids=input_text["input_ids"],
                 max_length=150,
                 num_return_sequences=1
             )
@@ -86,8 +82,10 @@ def generate_advice(extracted_data):
         return recommendations
 
     except Exception as e:
+        print(f"Error: {e}")  # Debugging any unexpected issues
         return [{"error": f"Exception occurred: {str(e)}"}]
 
+
 # Extract structured data from the PDF
 def pdf_to_text(pdf_file):
     try: