Spaces:
Runtime error
Runtime error
| from dotenv import load_dotenv | |
| import streamlit as st | |
| from user_utils import * | |
| #Creating session 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'] =[] | |
| load_dotenv() | |
| def main(): | |
| 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 | |
| index=pull_from_pinecone(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 to create a ticket with respective department | |
| button = st.button("Submit ticket?") | |
| if button: | |
| #Get Response | |
| 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) | |
| if __name__ == '__main__': | |
| main() | |