app.py

import streamlit as st
from streamlit_chat import message
from langchain_google_genai import ChatGoogleGenerativeAI
import os
import PyPDF2
import docx
import requests
from bs4 import BeautifulSoup
import re

# Set up Gemini AI
GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "AIzaSyDs-vtZBkyLmEUH5NgkfUNkMJ8kxg_pR3Y")
chat_model = ChatGoogleGenerativeAI(model="gemini-1.5-pro", api_version="v1",
    google_api_key=GEMINI_API_KEY)

# Set page config
st.set_page_config(page_title="AI-Driven Job Assistant", layout="wide")

# Initialize session state for messages if not set
if "messages" not in st.session_state:
    st.session_state.messages = []
if "resume_text" not in st.session_state:
    st.session_state.resume_text = ""

# Extract text from resume
def extract_text_from_resume(resume_file):
    text = ""
    if resume_file.name.endswith(".pdf"):
        pdf_reader = PyPDF2.PdfReader(resume_file)
        for page in pdf_reader.pages:
            extracted_text = page.extract_text()
            if extracted_text:
                text += extracted_text + "\n"
    elif resume_file.name.endswith(".docx"):
        doc = docx.Document(resume_file)
        for para in doc.paragraphs:
            text += para.text + "\n"
    return text.strip()

# Sidebar resume upload
with st.sidebar:
    st.header("Upload Your Resume")
    resume = st.file_uploader("Upload PDF or DOCX", type=["pdf", "docx"])
    if resume:
        st.success("Resume uploaded successfully!")
        if st.button("Proceed"):
            with st.spinner("Analyzing your resume..."):
                resume_text = extract_text_from_resume(resume)
                if resume_text:
                    st.session_state.resume_text = resume_text
                    ats_prompt = f"Analyze this resume and provide an ATS score (out of 100) along with improvement suggestions keep the response in short:\n\n{resume_text}"
                    ats_response = chat_model.predict(ats_prompt)
                    st.session_state.messages.append({"text": ats_response, "is_user": False})
                    st.session_state["resume_analyzed"] = True  # Mark resume as analyzed
                    st.rerun()
                else:
                    st.error("Could not extract text from the uploaded resume.")

# Main chat interface
st.title("💬 AI-Driven Job Assist")
st.subheader("ATS insights & Chatbot Assist")

# Display chat messages
for msg in st.session_state.messages:
    message(msg["text"], is_user=msg["is_user"])

# Ask for aspired job role after ATS score
if "resume_analyzed" in st.session_state and "aspired job role" not in st.session_state:
    ai_response = "What is your aspired job role?"
    st.session_state.messages.append({"text": ai_response, "is_user": False})
    del st.session_state["resume_analyzed"]  # Remove flag after asking
    st.rerun()

# Input area
if "user_message" not in st.session_state:
    st.session_state.user_message = ""


user_input = st.text_input("Type your message here...", key="user_message")

if st.button("Send") and user_input:
    st.session_state.messages.append({"text": user_input, "is_user": True})
    
    # AI response logic
    if "aspired job role" not in st.session_state:
        st.session_state["aspired job role"] = user_input
        job_role = st.session_state["aspired job role"]
        skills_prompt = f"For the job role of {job_role}, suggest the essential skills and any missing skills based on the user's resume:\n\nResume:\n{st.session_state.resume_text}\n\nAlso, provide relevant learning resources for upskilling with valid links of courses.keep the response in short"
        ai_response = chat_model.predict(skills_prompt)
    
    st.session_state.messages.append({"text": ai_response, "is_user": False})
    st.session_state.pop("user_message", None)
    st.rerun()

# Fetch jobs from LinkedIn
def fetch_jobs_from_linkedin(keyword, location, max_results=5):
    search_url = f"https://www.linkedin.com/jobs/search?keywords={keyword}&location={location}"
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
    }
    
    response = requests.get(search_url, headers=headers)
    if response.status_code != 200:
        st.error("Failed to fetch jobs from LinkedIn. Try again later.")
        return []
    
    soup = BeautifulSoup(response.text, "html.parser")
    job_listings = []
    
    for job_card in soup.find_all("div", class_="base-card")[:max_results]:
        title_tag = job_card.find("h3", class_="base-search-card__title")
        company_tag = job_card.find("h4", class_="base-search-card__subtitle")
        location_tag = job_card.find("span", class_="job-search-card__location")
        link_tag = job_card.find("a", class_="base-card__full-link")
        
        if title_tag and company_tag and location_tag and link_tag:
            job_listings.append({
                "title": title_tag.text.strip(),
                "company": company_tag.text.strip(),
                "location": location_tag.text.strip(),
                "link": link_tag["href"].strip(),
                "recruiter_email": fetch_recruiter_email(job_card)  # Placeholder function
            })
    
    return job_listings

# Placeholder function for fetching recruiter email
def fetch_recruiter_email(job_card):
    # Replace this with actual logic to find recruiter emails
    email_tag = job_card.find("span", class_="recruiter-email")  # Example selector
    return email_tag.text.strip() if email_tag else None

# Function to generate cold email and cover letter
def generate_email_and_cover_letter(job_title, company, recruiter_email):
    cold_email = f"""
    Subject: Application for {job_title} Position at {company}
    
    Dear Hiring Manager,
    
    I am excited to apply for the {job_title} position at {company}. With my skills and experience, I believe I am a great fit for this role. I have attached my resume for your reference and would love the opportunity to discuss further.
    
    Looking forward to your response.
    
    Best Regards,
    [Your Name]
    """
    
    cover_letter = f"""
    Dear Hiring Manager,
    
    I am writing to express my interest in the {job_title} position at {company}. I have a strong background in [mention relevant skills] and believe my expertise aligns with the job requirements.
    
    I am eager to bring my skills to your esteemed company and contribute effectively. Please find my resume attached for review.
    
    Thank you for your time and consideration. I look forward to the opportunity to speak with you.
    
    Sincerely,
    [Your Name]
    """
    
    return cold_email, cover_letter

# Function to generate LinkedIn message & connection request note
def generate_linkedin_outreach(job_title, company):
    linkedin_message = f"""
    Hi [Recruiter Name],

    I hope you're doing well. I came across the {job_title} opening at {company} and I am very interested. 
    I would love to connect and learn more about this opportunity. 
    Looking forward to your response!

    Best,  
    [Your Name]
    """

    connection_request_note = f"""
    Hi [Recruiter Name], I’m interested in the {job_title} role at {company} and would love to connect.
    """

    return linkedin_message, connection_request_note

# Streamlit UI
st.title("📌 Job Listings from LinkedIn")
keyword = st.text_input("Job Title (e.g., Data Scientist)", value=st.session_state.get("aspired job role", ""))
location = st.text_input("Location (e.g., New York)")

if st.button("Fetch Jobs"):
    jobs = fetch_jobs_from_linkedin(keyword, location)
    if jobs:
        for job in jobs[:3]:
            st.markdown(f"**{job['title']}** at {job['company']} ({job['location']})")
            st.markdown(f"[Apply Now]({job['link']})")

            # Call Gemini to get match score
            match_prompt = f"""Based on the following job role and resume, provide a match score out of 100 indicating how well the resume fits the job. Also give a 1-line reason for the score.
            Job Role: {job['title']} at {job['company']} in {job['location']}
            
            Resume: {st.session_state.resume_text}
            Keep the response short and structured like this:
            Score: 85
            Reason: Strong experience in React and REST APIs aligns well.
            """
            match_response = chat_model.predict(match_prompt)
            
            # Display the match score. Extract score and reason from the response
            match = re.search(r"Score:\s*(\d+)\s*\nReason:\s*(.*)", match_response)
            if match:
                score = int(match.group(1))
                reason = match.group(2)
                
                # Use color coding for different match levels
                if score >= 80:
                    color = "green"
                elif score >= 50:
                    color = "orange"
                else:
                    color = "red"
                    
                # Display match score with highlight
                st.markdown(f"🔍 **Resume Match Score:** <span style='color:{color}; font-size:22px; font-weight:bold'>{score}/100</span>", unsafe_allow_html=True)
                # Display reason separately
                st.markdown(f"📌 **Reason:** {reason}")
            else:
                st.markdown("⚠️ Could not extract match score. Please check the response format.")


            
            # Show recruiter email if available
            if job['recruiter_email']:
                st.markdown(f"**Recruiter Email:** {job['recruiter_email']}")
                cold_email, cover_letter = generate_email_and_cover_letter(job['title'], job['company'], job['recruiter_email'])
                
                with st.expander("📧 Suggested Cold Email"):
                    st.code(cold_email)
                
                with st.expander("📜 Suggested Cover Letter"):
                    st.code(cover_letter)
            else:
                st.markdown(f"**Recruiters Email not available**")

                # Provide LinkedIn message & connection note
                linkedin_msg, connection_note = generate_linkedin_outreach(job['title'], job['company'])
                
                with st.expander("💬 Suggested LinkedIn Message"):
                    st.code(linkedin_msg)
                
                with st.expander("🔗 Connection Request Note"):
                    st.code(connection_note)
    else:
        st.write("No jobs found. Try a different search.")