Update app.py

app.py

@@ -90,16 +90,19 @@ QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],templat
 llm_model = "deepset/roberta-base-squad2"
 from transformers import AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained(llm_model)
+from transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained(llm_model)
+
 #question = "How can I reverse diabetes?"
 
 #pipe = pipeline(model = llm_model, tokenizer = tokenizer, task = "text-generation", temperature=0.2)
 
 from langchain.chains.question_answering import load_qa_chain
 
-pipe = load_qa_chain(llm=llm_model,tokenizer =tokenizer, chain_type="map_reduce")
+#pipe = load_qa_chain(llm=llm_model,tokenizer =tokenizer, chain_type="map_reduce")
 print("check2")
 qa = ConversationalRetrievalChain.from_llm(
-    pipe,
+    model,
     retriever=retriever,
     memory=memory,
     chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}