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###
# Elo based comparison of models
# https://chat.lmsys.org/?leaderboard
###
##
# visual libraries gradio , could be streamlit as well or cl
##
import gradio as gr
##
# Libraries
# Langchain - https://python.langchain.com/docs/get_started/introduction.html
# Used for simplifiing calls, task
##
import langchain
import transformers
# https://huggingface.co/spaces/joyson072/LLm-Langchain/blob/main/app.py
from langchain.llms import HuggingFaceHub
# for the chain and prompt
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, SimpleSequentialChain
###################
llm = HuggingFaceHub(
repo_id="google/flan-ul2",
# repo_id="google/flan-t5-small",
model_kwargs={"temperature":0.1,
"max_new_tokens":250})
# Chain 1: Generating a rephrased version of the user's question
template = """{question}\n\n"""
prompt_template = PromptTemplate(input_variables=["question"], template=template)
question_chain = LLMChain(llm=llm, prompt=prompt_template)
# Chain 2: Generating assumptions made in the statement
template = """Here is a statement:
{statement}
Make a bullet point list of the assumptions you made when producing the above statement.\n\n"""
prompt_template = PromptTemplate(input_variables=["statement"], template=template)
assumptions_chain = LLMChain(llm=llm, prompt=prompt_template)
assumptions_chain_seq = SimpleSequentialChain(
chains=[question_chain, assumptions_chain], verbose=True
)
# Chain 3: Fact checking the assumptions
template = """Here is a bullet point list of assertions:
{assertions}
For each assertion, determine whether it is true or false. If it is false, explain why.\n\n"""
prompt_template = PromptTemplate(input_variables=["assertions"], template=template)
fact_checker_chain = LLMChain(llm=llm, prompt=prompt_template)
fact_checker_chain_seq = SimpleSequentialChain(
chains=[question_chain, assumptions_chain, fact_checker_chain], verbose=True
)
# Final Chain: Generating the final answer to the user's question based on the facts and assumptions
template = """In light of the above facts, how would you answer the question '{}'""".format(
"What is the capitol of the usa?"
# user_question
)
template = """{facts}\n""" + template
prompt_template = PromptTemplate(input_variables=["facts"], template=template)
answer_chain = LLMChain(llm=llm, prompt=prompt_template)
overall_chain = SimpleSequentialChain(
chains=[question_chain, assumptions_chain, fact_checker_chain, answer_chain],
verbose=True,
)
#print(overall_chain.run("What is the capitol of the usa?"))
##################
#import model class and tokenizer
from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
###
# Definition of different purspose prompts
# https://huggingface.co/spaces/Chris4K/rlhf-arena/edit/main/app.py
####
def prompt_human_instruct(system_msg, history):
return system_msg.strip() + "\n" + \
"\n".join(["\n".join(["###Human: "+item[0], "###Assistant: "+item[1]])
for item in history])
def prompt_instruct(system_msg, history):
return system_msg.strip() + "\n" + \
"\n".join(["\n".join(["### Instruction: "+item[0], "### Response: "+item[1]])
for item in history])
def prompt_chat(system_msg, history):
return system_msg.strip() + "\n" + \
"\n".join(["\n".join(["USER: "+item[0], "ASSISTANT: "+item[1]])
for item in history])
def prompt_roleplay(system_msg, history):
return "<|system|>" + system_msg.strip() + "\n" + \
"\n".join(["\n".join(["<|user|>"+item[0], "<|model|>"+item[1]])
for item in history])
####
## Sentinent models
# https://huggingface.co/spaces/CK42/sentiment-model-comparison
# 1, 4 seem best for german
####
model_id_1 = "nlptown/bert-base-multilingual-uncased-sentiment"
model_id_2 = "microsoft/deberta-xlarge-mnli"
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_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 }
#)
from transformers import pipeline
#
## Possible pipeline
#"['audio-classification', 'automatic-speech-recognition', 'conversational', 'depth-estimation', 'document-question-answering',
#'feature-extraction', 'fill-mask', 'image-classification', 'image-segmentation', 'image-to-text', 'mask-generation', 'ner',
#'object-detection', 'question-answering', 'sentiment-analysis', 'summarization', 'table-question-answering', 'text-classification',
#'text-generation', 'text2text-generation', 'token-classification', 'translation', 'video-classification', 'visual-question-answering',
#'vqa', 'zero-shot-audio-classification', 'zero-shot-classification', 'zero-shot-image-classification', 'zero-shot-object-detection',
#'translation_XX_to_YY']"
##
sentiment_pipe = pipeline("sentiment-analysis", model=model_id_7)
#pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
def pipeline_predict_sentiment(text):
sentiment_result = sentiment_pipe(text)
print(sentiment_result)
return sentiment_result
chat_pipe = pipeline("conversational")
def pipeline_predict_chat(text):
sentiment_result = chat_pipe(text)
print(sentiment_result)
return sentiment_result
#['huggingface', 'models', 'spaces']
#sentiment = gr.load(model_id_7, src="huggingface")
#def sentiment (message):
# sentiment_label = sentiment.predict(message)
# print ( sentiment_label)
# return sentiment_label
#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 ( message):
# sentiment_chain = LLMChain(llm=llm_hf_sentiment, prompt=sentiment_prompt)
# facts = sentiment_chain.run(message)
# print(facts)
# return facts
####
## 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"
text = "Why did the chicken cross the road?"
#output_question_1 = llm_hf(text)
#print(output_question_1)
###
## FACT EXTRACTION
###
# https://colab.research.google.com/drive/1hrS6_g14EcOD4ezwSGlGX2zxJegX5uNX#scrollTo=NUwUR9U7qkld
llm_factextract = HuggingFaceHub(
# repo_id="google/flan-ul2",
repo_id="google/flan-t5-small",
model_kwargs={"temperature":0.1,
"max_new_tokens":250})
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 factextraction (message):
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)
print(result)
return tokenizer.decode(result[0])
app = gr.Interface(
fn=func,
title="Conversation Bota",
inputs=["text", "checkbox", gr.Slider(0, 100)],
outputs=["text", "number"],
)
#app.launch()
####################
#app_sentiment = gr.Interface(fn=predict , inputs="textbox", outputs="textbox", title="Conversation Bot")
# create a public link, set `share=True` in `launch()
#app_sentiment.launch()
####################
###
###
###
classifier = pipeline("zero-shot-classification")
text = "This is a tutorial about Hugging Face."
candidate_labels = ["inform", "sell", "beschweren"]
def topic_sale_inform (text):
res = classifier(text, candidate_labels)
print (res)
return res
####
conversation = Conversation("Welcome")
def callChains(current_message):
sentiment_analysis_result = pipeline_predict_sentiment(current_message)
topic_sale_inform_result = topic_sale_inform(current_message)
#conversation.append_response("The Big lebowski.")
#conversation.add_user_input("Is it good?")
final_answer = func(current_message)
return final_answer, sentiment_analysis_result, topic_sale_inform_result
chat_bot = gr.Interface(fn=callChains , inputs="textbox", outputs=["textbox","textbox","textbox"], title="Conversation Bot with extra")
# create a public link, set `share=True` in `launch()
chat_bot.launch()
####################
app_facts = gr.Interface(fn=factextraction , inputs="textbox", outputs="textbox", title="Conversation Bots")
# create a public link, set `share=True` in `launch()
#app_facts.launch()
####################
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