Update app.py

app.py

@@ -62,14 +62,14 @@ tokenizer = AutoTokenizer.from_pretrained(llm_model)
 model = AutoModelForCausalLM.from_pretrained(llm_model)
 pipe = pipeline(model = llm_model, tokenizer = tokenizer,trust_remote_code=True, task = "question-answering", temperature=0.2)
 
-question = "How can I reverse diabetes?"
+#question = "How can I reverse diabetes?"
 #docs1 = retriever.invoke(question)
-docs1 = retriever.similarity_search(question)
-print(docs1[0].page_content)
+#docs1 = retriever.similarity_search(question)
+$print(docs1[0].page_content)
 
 import pandas as pd
-df = pd.DataFrame(docs1, columns=["text"])
-context =  df.to_string()
+#df = pd.DataFrame(docs1, columns=["text"])
+#context =  df.to_string()
 print(context)
 
 #print(docs1)[0]['generated_text'][-1]
@@ -81,12 +81,12 @@ print("check2")
 print("result")
 
 print("check3")
-chain = pipe(question = question,context = "Use the following information to answer the question. {context}.") 
-#chain = pipe(question = question,context = "Use the following information to answer the question. Diabetes can be cured by eating apples.") 
+#chain = pipe(question = question,context = "Use the following information to answer the question. {context}.") 
+chain = pipe(question = question,context = "Use the following information to answer the question. Diabetes can be cured by eating apples.") 
 
 
 print("check3A")
-print(chain)[0]['generated_text'][-1]
+#print(chain)[0]['generated_text'][-1]
 print("check3B")
 
 import gradio as gr