app.py

import streamlit as st
from transformers import pipeline
from PIL import Image
from datetime import time as t
import time

from operator import itemgetter  
import os
import json
import getpass
  
from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings  
import pinecone


from results import results_agent
from reranker import reranker
from utils import build_filter

OPENAI_API = st.secrets["OPENAI_API"]
PINECONE_API = st.secrets["PINECONE_API"]

pinecone.init(
    api_key= PINECONE_API,
    environment="gcp-starter" 
)
index_name = "use-class-db"

embeddings = OpenAIEmbeddings()

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"])

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)
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)

print(results_agent(result_query))

### GPT Response
# Display assistant response in chat message container
with st.chat_message("assistant"):
    message_placeholder = st.empty()
    full_response = ""
    assistant_response = "Hello there! How can I assist you today?"
    # 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})