Chris4K commited on
Commit
a733a2f
1 Parent(s): 62f7ef0

Update app.py

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Files changed (1) hide show
  1. app.py +37 -5
app.py CHANGED
@@ -244,6 +244,38 @@ app = gr.Interface(
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  outputs=["text", "number"],
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  )
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  ####
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  from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TextIteratorStreamer
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  from threading import Thread
@@ -349,12 +381,12 @@ def topic_sale_inform (text):
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  #conversation = Conversation("Welcome")
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  def callChains(current_message):
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- final_answer = generate(current_message, 1.0, 256, 0.9, 1.0)
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- sentiment_analysis_result = "3"#pipeline_predict_sentiment(current_message)
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- topic_sale_inform_result = "4"# topic_sale_inform(current_message)
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  #conversation.append_response("The Big lebowski.")
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  #conversation.add_user_input("Is it good?")
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- #final_answer = func(current_message)
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  return final_answer, sentiment_analysis_result, topic_sale_inform_result
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@@ -366,7 +398,7 @@ topic_sale_inform_result_inputfield = gr.Textbox(label="Thema ")
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  chat_bot = gr.Interface(fn=callChains , inputs=current_message_inputfield, outputs=[final_answer_inputfield,sentiment_analysis_result_inputfield,topic_sale_inform_result_inputfield], title="Conversation Bot with extra")
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  # create a public link, set `share=True` in `launch()
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- chat_bot.launch()
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  ####################
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  outputs=["text", "number"],
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  )
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+
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+
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+ #####
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+ tDeEn = pipeline(model="Helsinki-NLP/opus-mt-de-en")
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+ tEnDe = pipeline(model="Helsinki-NLP/opus-mt-en-de")
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+ bot = pipeline(model="google/flan-t5-large")
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+
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+ def solve(text,max_length,length_penalty,no_repeat_ngram_size,num_beams,language):
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+ if(language=="Deutsch"):
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+ text=tDeEn(text)[0]["translation_text"]
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+ out=bot(text,max_length=max_length, length_penalty=length_penalty, no_repeat_ngram_size=no_repeat_ngram_size, num_beams=num_beams, early_stopping=True)[0]["generated_text"]
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+ if(language=="Deutsch"):
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+ out=tEnDe(out)[0]["translation_text"]
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+ return out
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+
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+ task = gr.Interface(
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+ fn=solve,
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+ inputs=[
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+ gr.Textbox(lines=5,max_lines=6,label="Frage"),
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+ gr.Slider(minimum=1.0,maximum=200.0,value=50.0,step=1,interactive=True,label="max_length"),
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+ gr.Slider(minimum=1.0,maximum=20.0,value=1.0,step=1,interactive=True,label="length_penalty"),
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+ gr.Slider(minimum=0.0,maximum=5.0,value=3.0,step=1,interactive=True,label="no_repeat_ngram_size"),
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+ gr.Slider(minimum=1.0,maximum=20.0,value=1.0,step=1,interactive=True,label="num_beams"),
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+ gr.Dropdown(["Deutsch", "Englisch"],value="Deutsch"),
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+ ],
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+ outputs="text",
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+ title=title,
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+ description=desc,
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+ examples=examples
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+ ).launch()
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+
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+
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  ####
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  from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TextIteratorStreamer
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  from threading import Thread
 
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  #conversation = Conversation("Welcome")
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  def callChains(current_message):
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+ #final_answer = generate(current_message, 1.0, 256, 0.9, 1.0)
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+ sentiment_analysis_result = pipeline_predict_sentiment(current_message)
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+ topic_sale_inform_result = topic_sale_inform(current_message)
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  #conversation.append_response("The Big lebowski.")
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  #conversation.add_user_input("Is it good?")
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+ final_answer = func(current_message)
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  return final_answer, sentiment_analysis_result, topic_sale_inform_result
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  chat_bot = gr.Interface(fn=callChains , inputs=current_message_inputfield, outputs=[final_answer_inputfield,sentiment_analysis_result_inputfield,topic_sale_inform_result_inputfield], title="Conversation Bot with extra")
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  # create a public link, set `share=True` in `launch()
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+ #chat_bot.launch()
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  ####################
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