USC-GPT / app.py
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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
import re
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, clean_pinecone
from router import routing_agent
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 = 35
st.title("USC GPT - Find the perfect class")
class_time = st.slider(
"Filter Class Times:",
value=(t(8, 30), t(18, 45))
)
units = st.slider(
"Number of units",
1, 4, 4
)
days = st.multiselect("What days are you free?",
options = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat"],
default = None,
placeholder = "Any day"
)
assistant = st.chat_message("assistant")
initial_message = "Hello, I am your GPT-powered USC Class Helper! \n How can I assist you today?"
def get_rag_results(prompt):
'''
1. Remove filters from the prompt to optimize success of the RAG-based step.
2. Query the Pinecone DB and return the top 25 results based on cosine similarity
3. Rerank the results from vector DB using a BERT-based cross encoder
'''
query = filter_agent(prompt, OPENAI_API)
print("Here is the response from the filter_agent", query)
##Get metadata filters
days_filter = list()
start = float(class_time[0].hour) + float(class_time[0].minute) / 100.0
end = float(class_time[1].hour) + float(class_time[1].minute) / 100.0
query_filter = {
"start": {"$gte": start},
"end": {"$lte": end}
}
if units != "any":
query_filter["units"] = str(int(units)) + ".0 units"
if len(days) > 0:
for i in range(len(days)):
days_filter.append(days[i])
for j in range(i+1, len(days)):
two_day = days[i] + ", " + days[j]
days_filter.append(two_day)
query_filter["days"] = {"$in": days_filter}
## Query the pinecone database
response = index.query(
vector = embeddings.embed_query(query),
top_k = k,
filter = query_filter,
include_metadata = True
)
response, additional_metadata = clean_pinecone(response)
if len(response) < 1:
response = "No classes were found that matched your criteria"
additional_metadata = "None"
else:
response = reranker(query, response) # BERT cross encoder for ranking
return response, additional_metadata
if "messages" not in st.session_state:
st.session_state.messages = []
st.session_state.messages.append({"role": "assistant", "content": initial_message})
st.session_state.rag_responses = []
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?"):
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 = ""
messages = [{"role": m["role"], "content": m["content"]}
for m in st.session_state.messages[-6:]]
message_history = " ".join([message["content"] for message in messages])
print("Prompt is", prompt)
rag_response, additional_metadata = get_rag_results(prompt)
rag_response = " ".join([message for message in rag_response])
st.session_state.rag_responses.append(rag_response)
print("Here is the session state responses", st.session_state.rag_responses)
all_rag_responses = " ".join([response for response in st.session_state.rag_responses])
result_query = 'Original Query:' + prompt
# '\n Additional Class Times:' + str(additional_metadata)
assistant_response = results_agent(result_query, "Class Options from RAG:" + all_rag_responses + "\nMessage_history" + message_history)
# assistant_response = openai.ChatCompletion.create(
# model = "gpt-4",
# messages = [
# {"role": m["role"], "content": m["content"]}
# for m in st.session_state.messages
# ]
# )["choices"][0]["message"]["content"]
## Display response regardless of route
for chunk in re.split(r'(\s+)', assistant_response):
full_response += chunk + " "
time.sleep(0.02)
message_placeholder.markdown(full_response + "▌")
st.session_state.messages.append({"role": "assistant", "content": full_response})