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import re
import streamlit as st
from PyPDF2 import PdfReader
from dotenv import load_dotenv
from FindKeyword import FindKeyWords
from PreprocessText import preprocess_text
from model_Responce import model_prediction
from streamlit_extras.add_vertical_space import add_vertical_space
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
# from langchain.chat_models import ChatOpenAI
# from langchain.memory import ConversationBufferMemory
# from langchain.chains import ConversationalRetrievalChain
from htmlTemplates import css, bot_template, user_template
from InstructorEmbedding import INSTRUCTOR
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
def get_text_chunks(text):
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text)
return chunks
# Assuming this function encodes the question into a vector representation
def encode_question(question):
embeddings = HuggingFaceInstructEmbeddings() # Instantiate the embeddings model
question_vector = embeddings.embed_query(question) # Encode the question into a vector
return question_vector
# def handle_user_input(question):
# response = st.session_state.conversation({'question':question})
# st.session_state.chat_history = response('chat_history')
# for i,message in enumerate(st.session_state.chat_history):
# if i % 2 == 0:
# st.write(user_template.replace("{{MSG}}",message.content),unsafe_allow_html=True)
# else:
# st.write(bot_template.replace("{{MSG}}",message.content),unsafe_allow_html=True)
# def get_conversation_chain(vector_store):
# llm = ChatOpenAI()
# memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
# conversation_chain = ConversationalRetrievalChain.from_llm(
# llm=llm,
# retriever=vector_store.as_retriever(),
# memory = memory
# )
# return conversation_chain
def save_vector_store(text_chunks):
# embeddings = OpenAIEmbeddings()
# model = INSTRUCTOR('hkunlp/instructor-base')
# embeddings = model.encode(raw_text)
embeddings = HuggingFaceInstructEmbeddings()
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
new_db = FAISS.load_local("faiss_index_V2", embeddings)
new_db.merge_from(vectorstore)
new_db.save_local('faiss_index_V2')
return st.write("vector Store is Saved")
def button_function(all_text):
# Add your desired functionality here
# predictions = []
for item in all_text:
text = item['text']
# filename = item['filename']
pred = model_prediction(text)
# predictions.append({"filename": filename, "prediction": pred})
item['prediction'] = pred
return all_text
def get_pdf_text(pdfs,preprocess=True):
if preprocess:
all_text = []
for pdf in pdfs:
# Process each uploaded PDF file
# Reading PDF
pdf_reader = PdfReader(pdf)
# Get the filename of the PDF
filename = pdf.name
text = ""
# Reading Each Page
for page in pdf_reader.pages:
# Extracting Text in Every Page
text += page.extract_text()
# Preprocess the text
text = preprocess_text(text)
# Appending to array
all_text.append({"filename": filename, "text": text})
return all_text
else:
text = ""
for pdf in pdfs:
# Process each uploaded PDF file
# Reading PDF
pdf_reader = PdfReader(pdf)
# Reading Each Page
for page in pdf_reader.pages:
# Extracting Text in Every Page
text += page.extract_text()
# text = preprocess_text(text)
return text
def filter_keywords(all_text, keywords):
filtered_text = []
for item in all_text:
filename = item['filename']
text = item['text']
filtered_text_with_keywords = FindKeyWords(keywords, text)
filtered_text.append({"filename": filename, "text": filtered_text_with_keywords})
return filtered_text
# Main body
def main():
# vector_store = None
load_dotenv()
st.header("Resume Filter using Keywords 💬")
# Sidebar contents
with st.sidebar:
st.title('🤗💬 LLM Chat App')
# upload a PDF file
pdfs = st.file_uploader("Upload your Resumes", type='pdf',accept_multiple_files=True)
# Get user preference for matching keywords
# match_all_keywords = st.checkbox("Match All Keywords")
# Choose functionality: Prediction or Filtering
functionality = st.radio("Choose functionality:", ("Make Predictions", "Filter Keywords","Predict the Suitable canditate","Ask Questions"))
if functionality == "Ask Questions":
if st.button('Process'):
with st.spinner("Processing"):
# get pdf text
raw_text = get_pdf_text(pdfs, preprocess=False)
# get the text chunk
text_chunks = get_text_chunks(raw_text)
# create vector store
save_vector_store(text_chunks)
add_vertical_space(5)
st.write('Made with ❤️ by Fazni Farook')
if pdfs is not None:
all_text = get_pdf_text(pdfs)
# if 'conversation' not in st.session_state:
# st.session_state.conversation = None
# if 'chat_history' not in st.session_state:
# st.session_state.chat_history = None
if functionality == "Make Predictions":
if st.button('Make Prediction'):
with st.spinner("Progressing"):
all_text = button_function(all_text)
for item in all_text:
filename = item["filename"]
text = item["text"]
pred = item["prediction"]
st.markdown(f"**Filename: {filename}**")
# st.markdown(text, unsafe_allow_html=True)
st.markdown(f"**Prediction: {pred}**")
st.markdown("---")
elif functionality == "Filter Keywords":
# getting the keywords
keyword_input = st.text_input("Keyword")
keywords = [keyword.strip() for keyword in keyword_input.split(",")]
if st.button('Filter Keywords'):
with st.spinner("Progressing"):
filtered_text = filter_keywords(all_text, keywords)
for item in filtered_text:
filename = item["filename"]
text = item["text"]
st.markdown(f"**Filename: {filename}**")
st.markdown(text, unsafe_allow_html=True)
st.markdown("---")
elif functionality == "Predict the Suitable canditate":
# getting the keywords
keyword = st.text_input("Keyword")
if st.button('Filter Resumes'):
with st.spinner("Progressing"):
all_text = button_function(all_text)
# filtered_text = filter_keywords(all_text, keywords)
count = 0
for item in all_text:
filename = item["filename"]
prediction = item["prediction"]
if keyword.lower()==prediction.lower():
count+=1
st.markdown(f"**Filename: {filename}**")
st.markdown(prediction, unsafe_allow_html=True)
st.markdown("---")
if count==0:
st.markdown("No match found")
elif functionality == "Ask Questions":
embeddings = HuggingFaceInstructEmbeddings()
new_db = FAISS.load_local("faiss_index_V2", embeddings)
st.write(css,unsafe_allow_html=True)
# create conversation chain
# st.session_state.conversation = get_conversation_chain(vector_store)
question = st.text_input("Ask Question")
if st.button('Ask Question'):
with st.spinner("Processing"):
if question:
# Convert the question to a vector
question_vector = encode_question(question)
# Convert the vector store to a compatible format
output = new_db.similarity_search_by_vector(question_vector)
page_content = output[0].page_content
st.write(page_content)
if __name__=='__main__':
main() |