app.py

import streamlit as st
import os
import asyncio
import warnings
from crewai import Agent, Task, Crew
from langchain_google_genai import ChatGoogleGenerativeAI
from crewai_tools import FileReadTool, ScrapeWebsiteTool, SerperDevTool
import json

# Warning control
warnings.filterwarnings('ignore')

# Set the Google API key directly in the code
google_api_key = "AIzaSyCHHl4LgpZIBw3_Zf15XWuuLSWWIlLROdQ"

# Function to initialize the Gemini model with an event loop
async def initialize_llm():
    return ChatGoogleGenerativeAI(
        model="gemini-1.5-flash",
        verbose=True,
        temperature=0.5,
        google_api_key=google_api_key
    )

# Run the initialization within the event loop
if not hasattr(st, 'llm'):
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    st.llm = loop.run_until_complete(initialize_llm())

# Ensure the SERPER API key is correctly passed during initialization
serper_api_key = "uxSCra7XOaiojmWo3MIQ2xzFWkX2306yJY7YIVMsT771qLCaOJejKKQB7nKbRXd7"  # Replace with your actual API key
search_tool = SerperDevTool(api_key=serper_api_key)

# Initialize tools
scrape_tool = ScrapeWebsiteTool()
read_resume = FileReadTool(file_path='fake_resume.md')

# Define agents
researcher = Agent(
    role="Tech Job Researcher",
    goal="Make sure to do amazing analysis on job posting to help job applicants",
    tools=[scrape_tool, search_tool],
    verbose=True,
    backstory=(
        "As a Job Researcher, your prowess in navigating and extracting critical "
        "information from job postings is unmatched. Your skills help pinpoint the necessary "
        "qualifications and skills sought by employers, forming the foundation for effective application tailoring."
    ),
    llm=st.llm
)

profiler = Agent(
    role="Personal Profiler for Engineers",
    goal="Do incredible research on job applicants to help them stand out in the job market",
    tools=[scrape_tool, search_tool, read_resume],
    verbose=True,
    backstory=(
        "Equipped with analytical prowess, you dissect and synthesize information "
        "from diverse sources to craft comprehensive personal and professional profiles, laying the "
        "groundwork for personalized resume enhancements."
    ),
    llm=st.llm
)

resume_strategist = Agent(
    role="Resume Strategist for Engineers",
    goal="Find all the best ways to make a resume stand out in the job market.",
    tools=[scrape_tool, search_tool, read_resume],
    verbose=True,
    backstory=(
        "With a strategic mind and an eye for detail, you excel at refining resumes to highlight the most "
        "relevant skills and experiences, ensuring they resonate perfectly with the job's requirements."
    ),
    llm=st.llm
)

interview_preparer = Agent(
    role="Engineering Interview Preparer",
    goal="Create interview questions and talking points based on the resume and job requirements",
    tools=[scrape_tool, search_tool, read_resume],
    verbose=True,
    backstory=(
        "Your role is crucial in anticipating the dynamics of interviews. With your ability to formulate key questions "
        "and talking points, you prepare candidates for success, ensuring they can confidently address all aspects of the "
        "job they are applying for."
    ),
    llm=st.llm
)

# Define tasks
research_task = Task(
    description=(
        "Analyze the job posting URL provided ({job_posting_url}) "
        "to extract key skills, experiences, and qualifications required. Use the tools to gather content and identify "
        "and categorize the requirements."
    ),
    expected_output=(
        "A structured list of job requirements, including necessary skills, qualifications, and experiences."
    ),
    agent=researcher,
    async_execution=True
)

profile_task = Task(
    description=(
        "Compile a detailed personal and professional profile using the GitHub ({github_url}) URLs, LinkedIn ({linkedin_url}) URL, and personal write-up "
        "({personal_writeup}). Utilize tools to extract and synthesize information from these sources."
    ),
    expected_output=(
        "A comprehensive profile document that includes skills, project experiences, contributions, interests, and "
        "communication style."
    ),
    agent=profiler,
    async_execution=True
)

resume_strategy_task = Task(
    description=(
        "Using the profile and job requirements obtained from previous tasks, tailor the resume to highlight the most "
        "relevant areas. Employ tools to adjust and enhance the resume content. Make sure this is the best resume even but "
        "don't make up any information. Update every section, including the initial summary, work experience, skills, "
        "and education. All to better reflect the candidate's abilities and how it matches the job posting."
    ),
    expected_output=(
        "An updated resume that effectively highlights the candidate's qualifications and experiences relevant to the job."
    ),
    output_file="tailored_resume.md",
    context=[research_task, profile_task],
    agent=resume_strategist
)

interview_preparation_task = Task(
    description=(
        "Create a set of potential interview questions and talking points based on the tailored resume and job requirements. "
        "Utilize tools to generate relevant questions and discussion points. Make sure to use these questions and talking points to "
        "help the candidate highlight the main points of the resume and how it matches the job posting."
    ),
    expected_output=(
        "A document containing key questions and talking points that the candidate should prepare for the initial interview."
    ),
    output_file="interview_materials.md",
    context=[research_task, profile_task, resume_strategy_task],
    agent=interview_preparer
)

# Crew Setup
job_application_crew = Crew(
    agents=[researcher, profiler, resume_strategist, interview_preparer],
    tasks=[research_task, profile_task, resume_strategy_task, interview_preparation_task],
    verbose=True
)

# Job Application Inputs
job_application_inputs = {
    'job_posting_url': 'https://jobs.lever.co/AIFund/6c82e23e-d954-4dd8-a734-c0c2c5ee00f1?lever-origin=applied&lever-source%5B%5D=AI+Fund',
    'github_url': 'https://github.com/PrathameshK',
    'linkedin_url': 'https://www.linkedin.com/in/prathameshkhade/',
    'personal_writeup': 'fake_resume.md'
}

# Running Tasks
try:
    # Assuming job_application_crew.kickoff is an async function
    results = job_application_crew.kickoff(inputs=job_application_inputs)
    
    # Print type and contents for debugging
    st.write("Type of results:", type(results))
    st.write("Contents of results:", results)
    
    # Check if results is a dictionary
    if isinstance(results, dict):
        # Display results based on their expected keys
        st.write("Job Requirements:", results.get('job_requirements', 'No job requirements found'))
        st.write("Personal Profile:", results.get('personal_profile', 'No personal profile found'))
        st.write("Tailored Resume:", results.get('tailored_resume', 'No tailored resume found'))
        st.write("Interview Materials:", results.get('interview_materials', 'No interview materials found'))
    else:
        st.write("Unexpected results format:", results)
except Exception as e:
    st.error(f"An error occurred: {e}")