Update app.py

app.py

@@ -56,11 +56,28 @@ model_id_3 = "distilbert-base-uncased-finetuned-sst-2-english"
 model_id_4 = "lordtt13/emo-mobilebert"
 model_id_5 = "juliensimon/reviews-sentiment-analysis"
 model_id_6 = "sbcBI/sentiment_analysis_model"
-model_id_6 = "oliverguhr/german-sentiment-bert"
+model_id_7 = "oliverguhr/german-sentiment-bert"
+
+# https://colab.research.google.com/drive/1hrS6_g14EcOD4ezwSGlGX2zxJegX5uNX#scrollTo=NUwUR9U7qkld
+llm_hf_sentiment = HuggingFaceHub(
+    repo_id= model_id_7,
+    model_kwargs={"temperature":0.9 }
+)
+ 
+fact_extraction_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)
+  facts = sentiment_chain.run(message)
+  print(facts)
+  return facts
+
 
 ####
-## Chat models
-# https://huggingface.co/spaces/CK42/sentiment-model-comparison
+##   models
 # 1 seem best for testing
 ####
 #download and setup the model and tokenizer
@@ -68,14 +85,17 @@ model_name = 'facebook/blenderbot-400M-distill'
 tokenizer = BlenderbotTokenizer.from_pretrained(model_name)
 model = BlenderbotForConditionalGeneration.from_pretrained(model_name)
 
+
+
+####
+## Chat models
+# https://huggingface.co/spaces/CK42/sentiment-model-comparison
+# 1 seem best for testing
+####
 chat_model_facebook_blenderbot_400M_distill = "facebook/blenderbot-400M-distill"
 chat_model_HenryJJ_vincua_13b = "HenryJJ/vincua-13b"
 
-# https://colab.research.google.com/drive/1hrS6_g14EcOD4ezwSGlGX2zxJegX5uNX#scrollTo=NUwUR9U7qkld
-#llm_hf = HuggingFaceHub(
-#    repo_id= chat_model_HenryJJ_vincua_13b,
-#    model_kwargs={"temperature":0.9 }
-#)
+
 
 
 
@@ -89,17 +109,22 @@ text = "Why did the chicken cross the road?"
 ###
 ## FACT EXTRACTION
 ###
-
+# https://colab.research.google.com/drive/1hrS6_g14EcOD4ezwSGlGX2zxJegX5uNX#scrollTo=NUwUR9U7qkld
+llm_factextract = HuggingFaceHub(
+    repo_id="google/flan-ul2", 
+    model_kwargs={"temperature":0.1,
+                  "max_new_tokens":256})
+ 
 fact_extraction_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}"
 )
 
-#fact_extraction_chain = LLMChain(llm=llm_hf, prompt=fact_extraction_prompt)
-
-#facts = fact_extraction_chain.run(text + " " +output_question_1)
-
-#print(facts)
+def factextraction (llm_factextract, message):
+  fact_extraction_chain = LLMChain(llm=llm, prompt=fact_extraction_prompt)
+  facts = fact_extraction_chain.run(message)
+  print(facts)
+  return facts
 
 
 def func (message):
@@ -108,5 +133,5 @@ def func (message):
   return tokenizer.decode(result[0])
 
 import gradio as gr
-app = gr.Interface(fn=func, inputs="textbox", outputs="textbox", title="Conversation Bot")
+app = gr.Interface(fn=factextraction, inputs="textbox", outputs="textbox", title="Conversation Bot")
 app.launch()