| 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) | |
| button=st.button("submit ticket?") | |
| if button: | |
| st.write("raise ticket") | |
| # Ensure proper script execution entry point | |
| if __name__ == "__main__": | |
| main() | |