Create Job_application.py

Job_application.py

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+# Import required libraries
+import os
+import streamlit as st
+from crewai import Agent, Task, Crew, LLM
+from crewai_tools import (
+    SerperDevTool,
+    FileReadTool,
+    MDXSearchTool,
+    ScrapeWebsiteTool
+)
+from embedchain import App
+from embedchain.embedder import GeminiEmbedder
+
+# Set up API keys
+gemini_api_key = "AIzaSyDzsGJCnE3017fYnOM0Fp_aWs4YtN4d4TI"
+serper_api_key = "b86545fdabc35dcb13fd8cc0a9b88c3a17b6dc89"
+
+# Configure environment variables
+os.environ["GEMINIAI_API_KEY"] = gemini_api_key
+os.environ["SERPER_API_KEY"] = serper_api_key
+
+# Initialize Gemini embedding model
+gemini_embedder = GeminiEmbedder(api_key=gemini_api_key)
+
+# Initialize EmbedChain App with Gemini
+embedchain_app = App(embedding_model=gemini_embedder)
+
+# Initialize Tools
+search_tool = SerperDevTool()
+scrape_tool = ScrapeWebsiteTool()
+resume_file_path = 'resume.md'  # Use a relative or dynamic file path
+read_resume = FileReadTool(file_path=resume_file_path)
+semantic_search_resume = MDXSearchTool(mdx=resume_file_path, embedding_model=gemini_embedder, app=embedchain_app)
+
+# Agent 1: Researcher
+researcher = Agent(
+    role="Tech Job Researcher",
+    goal="Analyze job postings and extract required qualifications.",
+    tools=[scrape_tool, search_tool],
+    verbose=True,
+    backstory=(
+        "An expert in analyzing job postings, you identify essential skills "
+        "and qualifications required for job applications."
+    )
+)
+
+# Agent 2: Profiler
+profiler = Agent(
+    role="Personal Profiler for Engineers",
+    goal="Create a detailed profile for job applicants.",
+    tools=[read_resume, semantic_search_resume],
+    verbose=True,
+    backstory=(
+        "Specializing in building comprehensive profiles, you extract and "
+        "synthesize information to create impactful resumes."
+    )
+)
+
+# Agent 3: Resume Strategist
+resume_strategist = Agent(
+    role="Resume Strategist for Engineers",
+    goal="Refine resumes to align with job requirements.",
+    tools=[read_resume, semantic_search_resume],
+    verbose=True,
+    backstory=(
+        "Your expertise lies in crafting resumes that highlight key skills "
+        "and experiences to match job requirements."
+    )
+)
+
+# Agent 4: Interview Preparer
+interview_preparer = Agent(
+    role="Interview Preparer",
+    goal="Generate potential interview questions and talking points.",
+    tools=[read_resume, semantic_search_resume],
+    verbose=True,
+    backstory=(
+        "You prepare candidates for interviews by formulating relevant questions "
+        "and talking points based on the job and their profile."
+    )
+)
+
+# Define Tasks
+research_task = Task(
+    description="Analyze the job posting URL to extract key skills, qualifications, and requirements.",
+    expected_output="A structured list of job requirements.",
+    agent=researcher,
+    async_execution=True
+)
+
+profile_task = Task(
+    description="Create a detailed profile from the resume and personal write-up.",
+    expected_output="A comprehensive profile document.",
+    agent=profiler,
+    async_execution=True
+)
+
+resume_strategy_task = Task(
+    description="Tailor the resume based on job requirements and personal profile.",
+    expected_output="An updated resume tailored to the job.",
+    output_file="tailored_resume.md",
+    context=[research_task, profile_task],
+    agent=resume_strategist
+)
+
+interview_preparation_task = Task(
+    description="Generate interview questions and talking points based on the tailored resume.",
+    expected_output="A document with key interview questions and talking points.",
+    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
+)
+
+# Streamlit Application
+st.title("AI-Powered Job Application Assistant")
+
+# User Inputs
+st.header("Provide Job Details")
+job_posting_url = st.text_input("Job Posting URL", "https://jobs.lever.co/AIFund/6c82e23e-d954-4dd8-a734-c0c2c5ee00f1")
+github_url = st.text_input("GitHub Profile URL", "https://github.com/joaomdmoura")
+personal_writeup = st.text_area(
+    "Personal Writeup",
+    """Noah is an accomplished Software Engineering Leader with 18 years of experience, 
+    specializing in managing remote and in-office teams. He holds an MBA and has a strong 
+    background in AI and data science. Noah has successfully led major tech initiatives 
+    and startups, driving innovation and growth."""
+)
+
+# File Upload for Resume
+st.header("Upload Resume")
+uploaded_resume = st.file_uploader("Upload your resume (Markdown format)", type=["md"])
+if uploaded_resume:
+    resume_file_path = os.path.join("uploads", uploaded_resume.name)
+    with open(resume_file_path, "wb") as f:
+        f.write(uploaded_resume.getbuffer())
+    st.success("Resume uploaded successfully!")
+
+# Start Job Application Process
+if st.button("Start Job Application Process"):
+    job_application_inputs = {
+        'job_posting_url': job_posting_url,
+        'github_url': github_url,
+        'personal_writeup': personal_writeup
+    }
+
+    # Run the Crew
+    result = job_application_crew.kickoff(inputs=job_application_inputs)
+    st.success("Job Application Process Completed!")
+
+    # Display Results
+    if os.path.exists("tailored_resume.md"):
+        st.header("Generated Tailored Resume")
+        with open("tailored_resume.md", "r") as f:
+            st.markdown(f.read(), unsafe_allow_html=True)
+
+    if os.path.exists("interview_materials.md"):
+        st.header("Generated Interview Materials")
+        with open("interview_materials.md", "r") as f:
+            st.markdown(f.read(), unsafe_allow_html=True)