app.py

# Update imports
import streamlit as st
import requests
import json
import webbrowser
from io import StringIO
from bs4 import BeautifulSoup
import google.generativeai as genai
import os

# Initialize session state
if 'original_resume' not in st.session_state:
    st.session_state['original_resume'] = None
if 'keywords' not in st.session_state:
    st.session_state['keywords'] = None
if 'tailored_resume' not in st.session_state:
    st.session_state['tailored_resume'] = None

def scrape_website(url):
    response = requests.get(url)
    response.raise_for_status()
    soup = BeautifulSoup(response.text, 'html.parser')
    return soup.get_text()

def trim_text(text, max_length=3000):
    """Trim text while preserving important content"""
    if len(text) <= max_length:
        return text
    # Keep first and last parts
    half_length = max_length // 2
    return text[:half_length] + "..." + text[-half_length:]

# Configure Gemini
def init_gemini(api_key):
    genai.configure(api_key=api_key)
    generation_config = {
        "temperature": 0.7,
        "top_p": 0.95,
        "top_k": 40,
        "max_output_tokens": 8192,
        "response_mime_type": "application/json",
    }
    return genai.GenerativeModel(
        model_name="gemini-2.0-flash-exp",
        generation_config=generation_config
    )

def extract_keywords(job_description, model):
    prompt = f"""Extract key ATS keywords from job posting. Return JSON with 'high', 'medium', 'low' arrays:
    {job_description}"""
    
    response = model.generate_content(prompt)
    return json.loads(response.text)

def validate_resume_schema(resume_data, original_schema):
    """Validate and enforce resume schema consistency"""
    def get_schema_structure(data):
        if isinstance(data, dict):
            return {k: get_schema_structure(v) for k, v in data.items()}
        elif isinstance(data, list):
            return [get_schema_structure(data[0])] if data else []
        else:
            return type(data).__name__
    
    original_structure = get_schema_structure(original_schema)
    new_structure = get_schema_structure(resume_data)
    return original_structure == new_structure

def adapt_resume(resume_data, keywords, job_description, model, max_retries=3):
    original_schema = resume_data.copy()
    
    for attempt in range(max_retries):
        try:
            prompt = f"""As a CV expert, optimize the provided resume JSON for the target role. 
            Enhance sections (summary, experience, volunteer, interests, awards, projects, skills) by incorporating provided keywords:
            - High priority (3x weight)
            - Medium priority (2x weight)
            - Low priority (1x weight)
            Rules:
            - Keep all original facts and information
            - Maintain exact JSON structure and all existing keys
            - Use natural language from the keywords list
            - Do not add fictional content
            Base Schema: {json.dumps(original_schema)}
            Keywords: {json.dumps(keywords)}
            Job Description: {job_description}"""
            
            response = model.generate_content(prompt)
            tailored_resume = json.loads(response.text)
            
            if validate_resume_schema(tailored_resume, original_schema):
                return tailored_resume
                
        except Exception as e:
            if attempt == max_retries - 1:
                raise e
            
    raise ValueError("Schema validation failed")

def calculate_resume_match(resume_data, keywords):
    """Calculate match score between resume and keywords"""
    resume_text = json.dumps(resume_data).lower()
    total_score = 0
    matches = {'high': [], 'medium': [], 'low': []}
    
    # Weight multipliers for different priority levels
    weights = {"high": 3, "medium": 2, "low": 1}
    
    # Ensure keywords has the expected structure
    if not all(key in keywords for key in ['high', 'medium', 'low']):
        raise ValueError("Keywords must contain 'high', 'medium', and 'low' arrays")
    
    for priority in ['high', 'medium', 'low']:
        priority_score = 0
        priority_matches = []
        
        for word in keywords[priority]:
            word = word.lower()
            if word in resume_text:
                priority_score += weights[priority]
                priority_matches.append(word)
                
        matches[priority] = priority_matches
        total_score += priority_score
    
    # Normalize score to 0-100
    max_possible = sum(len(keywords[p]) * weights[p] for p in ['high', 'medium', 'low'])
    normalized_score = (total_score / max_possible * 100) if max_possible > 0 else 0
    
    return normalized_score, matches

def create_match_visualization(original_score, tailored_score, keywords, original_matches, tailored_matches):
    """Create visualization showing resume match comparison"""
    
    # Overall score comparison
    st.markdown("### 📊 Resume Match Analysis")
    
    # Score metrics side by side
    col1, col2 = st.columns(2)
    with col1:
        st.metric(
            "Original Resume Match Score", 
            f"{original_score:.1f}%"
        )
    with col2:
        st.metric(
            "Tailored Resume Match Score", 
            f"{tailored_score:.1f}%",
            delta=f"+{tailored_score - original_score:.1f}%"
        )

    # Keyword analysis by priority
    st.markdown("### 🎯 Keyword Matches")
    tabs = st.tabs(["High Priority 🔴", "Medium Priority 🟡", "Low Priority 🟢"])
    
    for idx, priority in enumerate(['high', 'medium', 'low']):
        with tabs[idx]:
            col1, col2 = st.columns(2)
            
            orig_matches = set(original_matches[priority])
            new_matches = set(tailored_matches[priority])
            added = new_matches - orig_matches
            
            # Original matches
            with col1:
                st.markdown("#### Original Matching Keywords")
                if orig_matches:
                    for keyword in orig_matches:
                        st.markdown(f"✓ `{keyword}`")
                else:
                    st.info("No matches found")
            
            # New matches
            with col2:
                st.markdown("#### Added the following Keywords")
                if added:
                    for keyword in added:
                        st.markdown(f"➕ `{keyword}`")
                else:
                    st.info("No new matches")

# Page config
st.set_page_config(page_title="Resume Tailor", page_icon="📄", layout="wide")

# Header
st.title("📄 Curriculum Customization Tool")
st.markdown("### Transform your resume for your dream job")
# Sidebar with API key
with st.sidebar:
    st.markdown("### 📝 How to Use")
    st.markdown("""
    1. **Prepare Your Resume**:
       - Create a basic resume at [rxresu.me](https://rxresu.me)
       - Export it as JSON (not PDF)
    
    2. **Get Job Details**:
       - Copy the job posting URL
    
    3. **Use the Tool**:
       - Upload your resume JSON
       - Paste the job URL
       - Click 'Tailor Resume'
    
    4. **Final Steps**:
       - Download the tailored JSON
       - Import back to rxresu.me
       - Export as PDF for application
    """)
    
    st.markdown("### ℹ️ About")
    st.markdown("""
    This tool uses Google's Gemini model to optimize your resume for ATS systems.
    
    🔓 Open Source: Feel free to modify and adapt this tool to your needs.
    The source code is available and customizable.
    
    📧 Contact: For questions or suggestions, reach out to:
    [email protected]
    """)
    
    # Disclaimer
    st.warning("""
    ⚠️ **Disclaimer**
    
    This tool is for educational purposes only. 
    AI-based tools can produce unexpected results. 
    Always verify the output before using.
    """)
    
    api_key = st.secrets["google_api_key"]
    if not api_key:
        st.error("API key not found in secrets. Please add your API key to the secrets.")

# Main input section
col1, col2 = st.columns(2)
with col1:
    job_url = st.text_input("Job Posting URL", placeholder="https://...")
with col2:
    resume_file = st.file_uploader("Upload Resume (JSON)", type="json")
    if resume_file:
        resume_str = StringIO(resume_file.getvalue().decode("utf-8"))
        st.session_state['original_resume'] = json.load(resume_str)

# Process button
if st.button("🎯 Tailor Resume", type="primary", use_container_width=True):
    if job_url and api_key and resume_file:
        try:
            with st.status("🔄 Processing...") as status:
                # Initialize Gemini
                model = init_gemini(api_key)
                
                # Rest of the processing remains the same, just using model instead of client
                status.update(label="Analyzing job posting...")
                job_description = scrape_website(job_url)
                keywords = extract_keywords(job_description, model)
                st.session_state['keywords'] = keywords
                
                status.update(label="Tailoring resume...")
                tailored_resume = adapt_resume(
                    st.session_state['original_resume'],
                    keywords,
                    job_description,
                    model
                )
                st.session_state['tailored_resume'] = tailored_resume
                status.update(label="✅ Done!", state="complete")

            # Results section
            st.markdown("---")
            st.markdown("## 📊 Results")
            
            # Calculate and display scores
            original_score, original_matches = calculate_resume_match(
                st.session_state['original_resume'],
                st.session_state['keywords']
            )
            tailored_score, tailored_matches = calculate_resume_match(
                st.session_state['tailored_resume'],
                st.session_state['keywords']
            )

            create_match_visualization(
                original_score,
                tailored_score,
                st.session_state['keywords'],
                original_matches,
                tailored_matches
            )

            # Download section
            st.markdown("### 📥 Download")
            if st.download_button(
                "⬇️ Download Tailored Resume",
                data=json.dumps(st.session_state['tailored_resume'], indent=4),
                file_name="tailored_resume.json",
                mime="application/json",
                use_container_width=True
            ):
                webbrowser.open_new_tab("https://rxresu.me/")
                st.info("📝 Resume Builder opened in new tab")

        except Exception as e:
            st.error(f"An error occurred: {str(e)}")
    else:
        st.error("Please provide all required inputs")