pages/05_Search_Candidates.py

import streamlit as st
import os
import tempfile
from utils.llm import model_pipeline, load_memory, typewriter
from utils.utils import load_documents
from dotenv import load_dotenv
load_dotenv()

st.title("Search the right candidates!")

if "messages" not in st.session_state:
    st.session_state.messages = []

if "memory" not in st.session_state:
    st.session_state["memory"] = load_memory()

uploaded_file = st.file_uploader("Choose a PDF...", type="pdf")
if uploaded_file is not None:
    # Create a temporary directory
    temp_dir = tempfile.mkdtemp()
    file_name = st.text_input("Enter File name: ", "uploaded_file.pdf")
    st.session_state["file_name"] = file_name
    # Save the uploaded file to the temporary directory
    with open(os.path.join(temp_dir, 'uploaded_file.pdf'), 'wb') as f:
        f.write(uploaded_file.getvalue())

    # Pass the file path to the load_documents function
    load_documents(file_path=os.path.join(temp_dir, 'uploaded_file.pdf'))
    st.session_state.messages.append({"role": "assistant", "content": "I have loaded the resume."})
    del uploaded_file

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

if query := st.chat_input("Whom are you looking for today?"):
    st.session_state.messages.append({"role": "user", "content": query})
    with st.chat_message("user"):
        st.markdown(query)
    with st.chat_message("assistant"):
        message_placeholder = st.empty()
        with st.spinner('Pulling out amazing candidates behind the bushes...'):
            full_response = ""
            chain = model_pipeline(st.session_state["memory"])
            response = chain.invoke(query)
        for key in response:
            st.markdown(f"##### :blue[{key}:] ")
            typewriter(response[key], key, speed=9)
            full_response += (f"##### :blue[{key}:]\n{response[key]}\n" or "")

    st.session_state.messages.append({"role": "assistant", "content": full_response})
    st.session_state["memory"].save_context({"query": query}, {"output": full_response})