| import streamlit as st | |
| from PIL import Image | |
| from datetime import time as t | |
| import time | |
| from operator import itemgetter | |
| import os | |
| import json | |
| import getpass | |
| import openai | |
| from langchain.vectorstores import Pinecone | |
| from langchain.embeddings import OpenAIEmbeddings | |
| import pinecone | |
| from results import results_agent | |
| from filter import filter_agent | |
| from reranker import reranker | |
| from utils import build_filter | |
| OPENAI_API = st.secrets["OPENAI_API"] | |
| PINECONE_API = st.secrets["PINECONE_API"] | |
| openai.api_key = OPENAI_API | |
| pinecone.init( | |
| api_key= PINECONE_API, | |
| environment="gcp-starter" | |
| ) | |
| index_name = "use-class-db" | |
| embeddings = OpenAIEmbeddings(openai_api_key = OPENAI_API) | |
| index = pinecone.Index(index_name) | |
| k = 5 | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [] | |
| st.title("USC GPT - Find the perfect class") | |
| class_time = st.slider( | |
| "Filter Class Times:", | |
| value=(t(11, 30), t(12, 45))) | |
| # st.write("You're scheduled for:", class_time) | |
| units = st.slider( | |
| "Number of units", | |
| 1, 4, | |
| value = (1, 4) | |
| ) | |
| # for message in st.session_state.messages: | |
| # with st.chat_message(message["role"]): | |
| # st.markdown(message["content"]) | |
| assistant = st.chat_message("assistant") | |
| assistant.write("How can I help you today?") | |
| def assistant_response(response): | |
| message_placeholder = st.empty() | |
| full_response = "" | |
| assistant_response = response | |
| # Simulate stream of response with milliseconds delay | |
| for chunk in assistant_response.split(): | |
| full_response += chunk + " " | |
| time.sleep(0.05) | |
| # Add a blinking cursor to simulate typing | |
| assistant.markdown(full_response + "▌") | |
| assistant.markdown(full_response) | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": full_response}) | |
| assistant_response("Yah I'm tired af right now boi") | |
| # if prompt := st.chat_input("What kind of class are you looking for?"): | |
| # # Display user message in chat message container | |
| # with st.chat_message("user"): | |
| # st.markdown(prompt) | |
| # # Add user message to chat history | |
| # st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # response = filter_agent(prompt, OPENAI_API) | |
| # query = response | |
| # response = index.query( | |
| # vector= embeddings.embed_query(query), | |
| # # filter= build_filter(json), | |
| # top_k=5, | |
| # include_metadata=True | |
| # ) | |
| # response = reranker(query, response) | |
| # result_query = 'Original Query:' + query + 'Query Results:' + str(response) | |
| # assistant_response = results_agent(result_query, OPENAI_API) | |
| # if assistant_response: | |
| # with st.chat_message("assistant"): | |
| # message_placeholder = st.empty() | |
| # full_response = "" | |
| # # Simulate stream of response with milliseconds delay | |
| # for chunk in assistant_response.split(): | |
| # full_response += chunk + " " | |
| # time.sleep(0.05) | |
| # # Add a blinking cursor to simulate typing | |
| # message_placeholder.markdown(full_response + "▌") | |
| # message_placeholder.markdown(full_response) | |
| # # Add assistant response to chat history | |
| # st.session_state.messages.append({"role": "assistant", "content": full_response}) | |