Refactor imports and update HuggingFaceEndpoint configuration in app.py

#1
Files changed (2) hide show
  1. app.py +13 -41
  2. requirements.txt +1 -2
app.py CHANGED
@@ -1,40 +1,23 @@
1
  import os
2
- import torch
3
- from transformers import (
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- BitsAndBytesConfig,
5
- pipeline
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- )
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  import streamlit as st
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  from langchain_community.vectorstores import FAISS
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  from langchain_community.embeddings import HuggingFaceEmbeddings
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- from langchain_community.llms import HuggingFacePipeline
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- from transformers import BitsAndBytesConfig
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- from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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- from langchain_community.llms import HuggingFaceEndpoint
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  from langchain.prompts import PromptTemplate
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  from langchain.schema.runnable import RunnablePassthrough
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  from langchain.chains import LLMChain
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- import transformers
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- from ctransformers import AutoModelForCausalLM, AutoTokenizer
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- import transformers
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- from transformers import pipeline
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- from datasets import load_dataset
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-
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- import transformers
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- token=st.secrets["HF_TOKEN"]
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  from huggingface_hub import login
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  login(token=st.secrets["HF_TOKEN"])
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- # Load model directly
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- from transformers import AutoTokenizer, AutoModelForCausalLM
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-
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- tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
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- model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
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  from langchain_community.document_loaders import TextLoader
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  from langchain_text_splitters import CharacterTextSplitter
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  from langchain_community.document_loaders import PyPDFLoader
 
 
 
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  # Montez Google Drive
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  loader = PyPDFLoader("test-1.pdf")
@@ -53,21 +36,6 @@ retriever = db.as_retriever(
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  )
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55
 
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- from langchain_community.llms import HuggingFacePipeline
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- from langchain.prompts import PromptTemplate
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- from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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-
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- text_generation_pipeline = transformers.pipeline(
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- model=model,
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- tokenizer=tokenizer,
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- task="text-generation",
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-
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- temperature=0.02,
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- repetition_penalty=1.1,
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- return_full_text=True,
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- max_new_tokens=512,
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- )
70
-
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  prompt_template = """
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  ### [INST]
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  Instruction: You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided without using prior knowledge.You answer in FRENCH
@@ -84,7 +52,11 @@ Answer in french only
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85
  """
86
 
87
- mistral_llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
 
 
 
 
88
 
89
  # Create prompt from prompt template
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  prompt = PromptTemplate(
@@ -93,7 +65,7 @@ prompt = PromptTemplate(
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  )
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95
  # Create llm chain
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- from langchain.chains import RetrievalQA
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98
 
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  retriever.search_kwargs = {'k':1}
@@ -111,7 +83,7 @@ st.title("Chatbot Interface")
111
 
112
  # Define function to handle user input and display chatbot response
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  def chatbot_response(user_input):
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- response = qa.get_answer(user_input)
115
  return response
116
 
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  # Streamlit components
@@ -124,4 +96,4 @@ if submit_button:
124
  bot_response = chatbot_response(user_input)
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  st.text_area("Bot:", value=bot_response, height=200)
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  else:
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- st.warning("Please enter a message.")
 
1
  import os
 
 
 
 
 
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  import streamlit as st
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  from langchain_community.vectorstores import FAISS
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  from langchain_community.embeddings import HuggingFaceEmbeddings
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+
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+ from langchain_huggingface import HuggingFaceEndpoint
 
 
7
 
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  from langchain.prompts import PromptTemplate
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  from langchain.schema.runnable import RunnablePassthrough
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  from langchain.chains import LLMChain
 
 
11
 
 
 
 
 
 
 
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  from huggingface_hub import login
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  login(token=st.secrets["HF_TOKEN"])
 
 
 
 
 
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  from langchain_community.document_loaders import TextLoader
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  from langchain_text_splitters import CharacterTextSplitter
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  from langchain_community.document_loaders import PyPDFLoader
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+ from langchain.chains import RetrievalQA
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+ from langchain.prompts import PromptTemplate
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+ from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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  # Montez Google Drive
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  loader = PyPDFLoader("test-1.pdf")
 
36
  )
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38
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  prompt_template = """
40
  ### [INST]
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  Instruction: You are a Q&A assistant. Your goal is to answer questions as accurately as possible based on the instructions and context provided without using prior knowledge.You answer in FRENCH
 
52
 
53
  """
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+ repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
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+
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+ mistral_llm = HuggingFaceEndpoint(
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+ repo_id=repo_id, max_length=128, temperature=0.5, huggingfacehub_api_token=st.secrets["HF_TOKEN"]
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+ )
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61
  # Create prompt from prompt template
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  prompt = PromptTemplate(
 
65
  )
66
 
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  # Create llm chain
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+ llm_chain = LLMChain(llm=mistral_llm, prompt=prompt)
69
 
70
 
71
  retriever.search_kwargs = {'k':1}
 
83
 
84
  # Define function to handle user input and display chatbot response
85
  def chatbot_response(user_input):
86
+ response = qa.run(user_input)
87
  return response
88
 
89
  # Streamlit components
 
96
  bot_response = chatbot_response(user_input)
97
  st.text_area("Bot:", value=bot_response, height=200)
98
  else:
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+ st.warning("Please enter a message.")
requirements.txt CHANGED
@@ -1,5 +1,4 @@
1
- bitsandbytes
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- peft
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  sentence_transformers
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  scipy
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  langchain
 
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+ langchain_huggingface
 
2
  sentence_transformers
3
  scipy
4
  langchain