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import streamlit as st
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
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)
)
assistant = st.chat_message("assistant")
initial_message = "How can I help you today?"
if "messages" not in st.session_state:
st.session_state.messages = []
with st.chat_message("assistant"):
st.markdown(initial_message)
st.session_state.messages.append({"role": "assistant", "content": initial_message})
if prompt := st.chat_input("What kind of class are you looking for?"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
response = filter_agent(prompt, OPENAI_API)
query = response
response = index.query(
vector = embeddings.embed_query(query),
top_k = 25,
include_metadata = True
)
response = reranker(query, response)
result_query = 'Original Query:' + query + 'Query Results:' + str(response)
assistant_response = results_agent(result_query, OPENAI_API)
for chunk in assistant_response.split():
full_response += chunk + " "
time.sleep(0.05)
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response})
# 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})
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