Update main.py

main.py

@@ -80,7 +80,7 @@ async def pdf_file_qa_process(user_question: str, request: Request, file_to_proc
     uploaded_file = file_to_process.uploaded_file
     print("File received:"+uploaded_file.filename)
 
-    username = request.query_params.get("user_question")
+    user_question = request.query_params.get("user_question")
     filename = request.query_params.get("filename")
     print("User entered question: "+user_question)
     print("User uploaded file: "+filename)   
@@ -115,7 +115,7 @@ async def pdf_file_qa_process(user_question: str, request: Request, file_to_proc
     print("db_embeddings created...")
 
     #question = var_query.query    
-    question = username
+    question = user_question
     print("API Call Query Received: "+question)
     q_embedding=get_embeddings(question)
     final_q_embedding = torch.FloatTensor(q_embedding)
@@ -143,7 +143,11 @@ async def pdf_file_qa_process(user_question: str, request: Request, file_to_proc
 
     loader = TextLoader(file_path, encoding="utf-8")
     loaded_documents = loader.load()
+    print("*****loaded_documents******")
     print(loaded_documents)
+    print("***********")
+    print(question)
+    print("*****question******")
 
     print("LLM Chain Starts...")        
     start_2 = timeit.default_timer()    
@@ -151,14 +155,19 @@ async def pdf_file_qa_process(user_question: str, request: Request, file_to_proc
     end_2 = timeit.default_timer()  
     print("LLM Chain Ends...")  
     print(f'LLM Chain共耗时： @ {end_2 - start_2}')  
+
+    print(temp_ai_response)
     
     initial_ai_response=temp_ai_response['output_text']
 
+    print(initial_ai_response)
+
     cleaned_initial_ai_response = remove_context(initial_ai_response)
     
     #final_ai_response = cleaned_initial_ai_response.partition('¿Cuál es')[0].strip().replace('\n\n', '\n').replace('<|end|>', '').replace('<|user|>', '').replace('<|system|>', '').replace('<|assistant|>', '')
     final_ai_response = cleaned_initial_ai_response.partition('¿Cuál es')[0].strip()
     final_ai_response = final_ai_response.partition('¿Cuáles')[0].strip()
+    final_ai_response = final_ai_response.partition('¿Qué es')[0].strip()
     final_ai_response = final_ai_response.partition('<|end|>')[0].strip().replace('\n\n', '\n').replace('<|end|>', '').replace('<|user|>', '').replace('<|system|>', '').replace('<|assistant|>', '')
     new_final_ai_response = final_ai_response.split('Unhelpful Answer:')[0].strip()
     new_final_ai_response = new_final_ai_response.split('Note:')[0].strip()