app.py

import gradio as gr

#|------------------------
#| requirements.txt file
#|------------------------
#| torch
#| transformers

import torch
import transformers
import gradio as gr
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
from langchain.llms import OpenAI
from transformers import AutoTokenizer,pipeline 
from langchain.chat_models import ChatOpenAI
from langchain.vectorstores import Chroma
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
import os

api_key = os.getenv('OPEN_API_KEY')
client = OpenAI(api_key=api_key)

#HF_TOKEN = os.getenv('HF_TOKEN')
#OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')


title = "QUESTION ANSWERIGN WITH RAG TECHNIQUES"
description = """
This Space uses:

fine tuned model
ChatGPT 3.5 

to answer to quesiton extracted from Enron dataset  
"""

examples=[
    ['how Top Gas Gorillas is performing?'],
    ['Can the commission use revenues?'],
    ['....'],
    ]

#query2='how Top Gas Gorillas is performing?'
#context2='Note the discussion on market share halfway through the article. Top Gas Gorillas Show Strong Volume Growth The year 2000 was a banner year for the top players in gas marketing, with huge increases in gas prices, enormous volatility, continuing growth in sales volumes and major potential for profits.'


#model_name = "sentence-transformers/all-MiniLM-L6-v2"
model_name = 'sentence-transformers/multi-qa-mpnet-base-dot-v1'
#device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_kwargs = {"device": "cpu"}
embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)



res_output=[]

def predict(query):
  persist_directory="./chroma_db3/"
  db2 = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
  db2.get() 
  docs = db2.similarity_search(query)
  context=docs[0]
  res_output.append(context)
  # retrieve the context with LLM

  question=query
  context=docs[0].page_content

  nlp_ft = pipeline("question-answering", model="FlavioBF/multi-qa-mpnet-base-dot-v1_fine_tuned_model")
  res_output.append(nlp_ft(question=question,context=context))


  # Build prompt
  template = """Use the context to answer the question at the end. If you don't know the answer, answer "I do not know the answer". Do not try to make up an answer. Use maximum two sentences maximum. \
  Keep the answer as concise as possible.
  {context}
  Question: {question}
  Answer:"""
  QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
  # retrieve the context
  llm_name = "gpt-3.5-turbo"
  llm = ChatOpenAI(model_name=llm_name, temperature=0,openai_api_key=api_key)
  qa_chain = RetrievalQA.from_chain_type(
      llm,
      retriever=db2.as_retriever()
  )  
  res_output.append(qa_chain({"query": question})) 
  return res_output


gr.Interface(
    fn=predict,

    inputs=[
        gr.Textbox(lines=2,label="Question"),
    ],
    examples=examples,
    title="Chat with Enron Dataset with RAG technique",
    description=description,
    outputs=[gr.Textbox(lines=8, label="CONTEXT"),
             gr.Textbox(lines=4,label="multi-qa-mpnet-base-dot-v1_fine_tuned_model"),
            gr.Textbox(lines=4, label="GPT 3.5 answer"),
             ]

).launch(share=True) #.launch(share=True)