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import streamlit as st
from dotenv import load_dotenv
from transformers import pipeline
from user_utils import *
# Load environment variables
# Initialize the question-answering pipeline with a pre-trained model
def main():
load_dotenv()
st.header("Automatic Ticket Classification Tool")
#Capture user input
st.write("We are here to help you, please ask your question:")
user_input = st.text_input("🔍")
if user_input:
#creating embeddings instance...
embeddings=create_embeddings()
#Function to pull index data from Pinecone
import os
#We are fetching the previously stored Pinecome environment variable key in "Load_Data_Store.py" file
index=pull_from_pinecone("pcsk_4etRhj_Lc37c2KWzUgdTSPaShQKgxeZvC331qJcVWjK9LfpDARwkG23kXZoN5ZCHVLyYWZ","us-east-1","ticket",embeddings)
#This function will help us in fetching the top relevent documents from our vector store - Pinecone Index
relavant_docs=get_similar_docs(index,user_input)
#This will return the fine tuned response by LLM
response=get_answer(relavant_docs,user_input)
st.write(response)
# Ensure proper script execution entry point
if __name__ == "__main__":
main()
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