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import streamlit as st
from dotenv import load_dotenv
from transformers import pipeline
from user_utils import *
# Load environment variables
if 'HR_tickets' not in st.session_state:
st.session_state['HR_tickets'] =[]
if 'IT_tickets' not in st.session_state:
st.session_state['IT_tickets'] =[]
if 'Transport_tickets' not in st.session_state:
st.session_state['Transport_tickets'] =[]
# 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)
button=st.button("submit ticket?")
if button:
embeddings = create_embeddings()
query_result = embeddings.embed_query(user_input)
#loading the ML model, so that we can use it to predit the class to which this compliant belongs to...
department_value = predict(query_result)
st.write("your ticket has been sumbitted to : "+department_value)
#Appending the tickets to below list, so that we can view/use them later on...
if department_value=="HR":
st.session_state['HR_tickets'].append(user_input)
elif department_value=="IT":
st.session_state['IT_tickets'].append(user_input)
else:
st.session_state['Transport_tickets'].append(user_input)
# Ensure proper script execution entry point
if __name__ == "__main__":
main()
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