Update app.py

app.py

@@ -64,26 +64,18 @@ llm_hf_sentiment = HuggingFaceHub(
     model_kwargs={"temperature":0.9 }
 )
  
-fact_extraction_prompt = PromptTemplate(
+sentiment_prompt = PromptTemplate(
     input_variables=["text_input"],
     template="Extract the key facts out of this text. Don't include opinions. Give each fact a number and keep them short sentences. :\n\n {text_input}"
 )
 
-def sentiment (llm_factextract, message):
-  sentiment_chain = LLMChain(llm=llm, prompt=sentiment_prompt)
+def sentiment (  message):
+  sentiment_chain = LLMChain(llm=llm_hf_sentiment, prompt=sentiment_prompt)
   facts = sentiment_chain.run(message)
   print(facts)
   return facts
 
 
-####
-##   models
-# 1 seem best for testing
-####
-#download and setup the model and tokenizer
-model_name = 'facebook/blenderbot-400M-distill'
-tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
-model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
 
 
 
@@ -121,12 +113,21 @@ fact_extraction_prompt = PromptTemplate(
 )
 
 def factextraction (message):
-  fact_extraction_chain = LLMChain(llm=factextraction, prompt=fact_extraction_prompt)
+  fact_extraction_chain = LLMChain(llm=llm_factextract, prompt=fact_extraction_prompt)
   facts = fact_extraction_chain.run(message)
   print(facts)
   return facts
 
 
+####
+##   models
+# 1 seem best for testing
+####
+#download and setup the model and tokenizer
+model_name = 'facebook/blenderbot-400M-distill'
+tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
+model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
+
 def func (message):
   inputs = tokenizer(message, return_tensors="pt")
   result = model.generate(**inputs)