app.py

import os

import streamlit as st
from crewai import Crew
from crewai_tools import PDFSearchTool, ScrapeWebsiteTool, SerperDevTool
from dotenv import load_dotenv
from PIL import Image

from src.agents import MultiAgents
from src.tasks import MultiTasks

# Load environment variables
load_dotenv()
os.environ["OPENAI_MODEL_NAME"] = "gpt-3.5-turbo"

# Initialize agents and tasks
agents = MultiAgents()
tasks = MultiTasks()
search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()

# Load icon image
image_icon = Image.open("src/assistente-de-robo.png")
image_icon = image_icon.resize((100, 100))


def save_uploaded_file(uploaded_file):
    temp_dir = "tempDir"
    if not os.path.exists(temp_dir):
        os.makedirs(temp_dir)
    temp_file_path = os.path.join(temp_dir, uploaded_file.name)
    with open(temp_file_path, "wb") as f:
        f.write(uploaded_file.getbuffer())
    return temp_file_path


def read_file(file_path):
    pdf_search_tool = PDFSearchTool(pdf=file_path)
    return pdf_search_tool


def main():
    with st.sidebar:
        st.title("Hello, I'm Taylor AI!\nYour Career Consultant:")
        st.write(
            """I am here to help you highlight your skills and
            experiences for the job market.I am currently using the gpt-3.
            5-turbo model."""
        )

        st.session_state.openai_api_key = st.text_input(
            "Enter your OpenAI token:", type="password"
        )

        st.session_state.serper_api_key = st.text_input(
            "Enter your SERPER token:", type="password"
        )

        st.image(image_icon, use_column_width=True)

        if st.session_state.openai_api_key:
            os.environ["OPENAI_API_KEY"] = st.session_state.openai_api_key
        if st.session_state.serper_api_key:
            os.environ["SERPER_API_KEY"] = st.session_state.serper_api_key

    st.header("Career Consultant")

    if "result_done" not in st.session_state:
        st.session_state.result_done = False
    if "result" not in st.session_state:
        st.session_state.result = None

    candidate_name = st.text_input("Enter your name:")
    job_posting_url = st.text_input("Enter the job posting URL:")
    github_url = st.text_input("Enter your GitHub URL:")
    uploaded_resume = st.file_uploader(
        "Please upload your resume in PDF format",
        type=["pdf"],
    )

    if uploaded_resume:
        if uploaded_resume.type == "application/pdf":
            temp_file_path = save_uploaded_file(uploaded_resume)
            pdf_search_tool = read_file(temp_file_path)
            os.remove(temp_file_path)

    if st.button("Perform Analysis"):
        # Agents
        researcher = agents.researcher(search_tool, scrape_tool)
        profile_creator = agents.profile_creator(
            search_tool, scrape_tool, pdf_search_tool
        )
        professional_consultant = agents.professional_consultant(
            search_tool, scrape_tool, pdf_search_tool
        )
        interview_preparer = agents.interview_preparer(
            search_tool, scrape_tool, pdf_search_tool
        )

        # Tasks
        research_task = tasks.research_task(researcher, job_posting_url)
        profile_manager_task = tasks.profile_manager_task(
            profile_creator, github_url, candidate_name
        )
        resume_adaptation_task = tasks.resume_adaptation_task(
            candidate_name,
            professional_consultant,
            profile_manager_task,
            profile_manager_task,
        )
        interview_preparation_task = tasks.interview_preparation_task(
            interview_preparer,
            research_task,
            profile_manager_task,
            resume_adaptation_task,
        )

        crew = Crew(
            agents=[
                researcher,
                profile_creator,
                professional_consultant,
                interview_preparer,
            ],
            tasks=[
                research_task,
                profile_manager_task,
                resume_adaptation_task,
                interview_preparation_task,
            ],
            verbose=True,
        )

        inputs = {
            "candidate_name": candidate_name,
            "github_url": github_url,
            "job_posting_url": job_posting_url,
            "uploaded_resume": uploaded_resume,
        }

        # Execute the analysis
        result = crew.kickoff(inputs=inputs)
        st.session_state.result_done = True
        st.session_state.result = result
        st.session_state.show_success = False

        st.write(st.session_state.result)
        resume_file_path = os.path.basename(
            f"custom_resume_{candidate_name}.md"
        )
        with open(resume_file_path, "rb") as file:
            btn = st.download_button(
                label="Download Generated Resume",
                data=file,
                file_name=os.path.basename(resume_file_path),
                mime="text/plain",
            )
            if btn:
                st.success("Download Started!")
        st.success(f"Analysis completed! Thank you, {candidate_name}!")


if __name__ == "__main__":
    main()