app.py

# app.py - Fixed version with proper adapter loading
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel, PeftConfig
import os

print("🚀 ATS Resume Optimizer - Starting...")

# Check adapter files
print("\n📋 Files in current directory:")
for f in os.listdir("."):
    print(f"  - {f}")

# Load model with proper config
print("\n📥 Loading model configuration...")

try:
    # Load PEFT config first to understand the adapter structure
    peft_config = PeftConfig.from_pretrained(".")
    print("✅ Adapter config loaded")
    
    # Load tokenizer
    print("\n📥 Loading tokenizer...")
    tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)
    tokenizer.pad_token = tokenizer.eos_token
    print("✅ Tokenizer loaded")
    
    # Load base model
    print("\n📥 Loading base model (this takes 2-3 minutes)...")
    model = AutoModelForCausalLM.from_pretrained(
        peft_config.base_model_name_or_path,
        torch_dtype=torch.float16,
        device_map="auto",
        low_cpu_mem_usage=True,
    )
    print("✅ Base model loaded")
    
    # Load adapters with proper config
    print("\n📥 Loading your fine-tuned adapters...")
    model = PeftModel.from_pretrained(
        model,
        ".",
        config=peft_config,
    )
    model.eval()
    
    print("✅ Fine-tuned model loaded successfully!")
    MODEL_LOADED = True
    
except Exception as e:
    print(f"❌ Error loading adapters: {e}")
    print("\n⚠️ Falling back to base model only")
    
    # Fallback to base model
    tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
    tokenizer.pad_token = tokenizer.eos_token
    
    model = AutoModelForCausalLM.from_pretrained(
        "mistralai/Mistral-7B-Instruct-v0.2",
        torch_dtype=torch.float16,
        device_map="auto",
        low_cpu_mem_usage=True,
    )
    
    MODEL_LOADED = False

def analyze_resume(resume_text, job_description):
    """Generate ATS analysis"""
    
    if not MODEL_LOADED:
        return """⚠️ **Using Base Model Only**
        
The fine-tuned adapters couldn't be loaded. The model will still work but responses may be less specific to ATS optimization.

To see the full fine-tuned version, please contact the developer.

---

**Analyzing with base Mistral-7B...**
"""
    
    if not resume_text or len(resume_text.strip()) < 50:
        return "⚠️ Please enter a resume (at least 50 characters)"
    
    if not job_description or len(job_description.strip()) < 30:
        return "⚠️ Please enter a job description (at least 30 characters)"
    
    # Truncate to fit context
    resume_text = resume_text[:1500]
    job_description = job_description[:800]
    
    prompt = f"""<s>[INST] Analyze this resume for ATS compatibility with the job description. Provide an ATS score, identify missing keywords, and suggest improvements.

RESUME:
{resume_text}

JOB DESCRIPTION:
{job_description} [/INST]

"""
    
    try:
        inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
        
        # Move to device
        if torch.cuda.is_available():
            inputs = {k: v.cuda() for k, v in inputs.items()}
        
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=800,
                temperature=0.7,
                top_p=0.9,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id,
                eos_token_id=tokenizer.eos_token_id,
            )
        
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Extract response
        if "[/INST]" in response:
            response = response.split("[/INST]")[1].strip()
        
        return response
    
    except Exception as e:
        return f"❌ Error: {str(e)}\n\nPlease try with shorter text."

# Sample data
SAMPLE_RESUME = """Sarah Johnson
Email: sarah.j@email.com | Phone: (555) 234-5678

PROFESSIONAL SUMMARY
Software Engineer with 3+ years of experience in full-stack development.

TECHNICAL SKILLS
Languages: Python, JavaScript, TypeScript
Frontend: React, HTML5, CSS3
Backend: Node.js, Express
Databases: PostgreSQL, MongoDB
Tools: Git, Docker, AWS

EXPERIENCE

Software Engineer | TechCorp | 2021 - Present
• Built web applications serving 100K+ users
• Improved performance by 40%
• Implemented CI/CD pipelines
• Collaborated in Agile teams

Junior Developer | StartupXYZ | 2020 - 2021
• Developed REST APIs
• Created responsive UIs
• Fixed bugs and added features

EDUCATION
BS Computer Science | State University | 2020
"""

SAMPLE_JOB = """Position: Senior Full Stack Developer

Required Skills:
• React, TypeScript, JavaScript
• Node.js, Express
• MongoDB or PostgreSQL
• REST API design
• Git, Docker, AWS
• Agile methodologies

Experience: 3-5 years

Responsibilities:
• Design and develop web applications
• Write clean, maintainable code
• Code reviews and mentoring
• Architecture decisions
"""

# Gradio interface
with gr.Blocks(title="ATS Resume Optimizer", theme=gr.themes.Soft()) as demo:
    
    gr.Markdown("""
    # 🎯 ATS Resume Optimizer
    
    ### AI-Powered Resume Analysis
    
    Get instant feedback on your resume:
    - ✅ **ATS Compatibility Score**
    - 🔍 **Missing Keywords**
    - 💡 **Optimization Suggestions**
    """)
    
    if not MODEL_LOADED:
        gr.Markdown("""
        > ⚠️ **Note:** Currently running with base model. Fine-tuned adapters couldn't be loaded.
        > The tool will still provide useful analysis but may be less specific.
        """)
    
    gr.Markdown("---")
    
    with gr.Row():
        with gr.Column():
            gr.Markdown("### 📄 Your Resume")
            resume_input = gr.Textbox(
                label="Paste Resume",
                placeholder="Copy and paste your resume...",
                lines=12,
                value=SAMPLE_RESUME
            )
            
        with gr.Column():
            gr.Markdown("### 💼 Job Description")
            job_input = gr.Textbox(
                label="Paste Job Description",
                placeholder="Copy and paste job description...",
                lines=12,
                value=SAMPLE_JOB
            )
    
    analyze_btn = gr.Button("🚀 Analyze Resume", variant="primary", size="lg")
    
    gr.Markdown("### 📊 Analysis Results")
    output = gr.Textbox(
        label="ATS Analysis",
        lines=15,
        show_copy_button=True
    )
    
    gr.Markdown("""
    ---
    ### 💡 How to Use
    
    1. **Paste your resume** in the left box (or try the sample)
    2. **Paste job description** in the right box
    3. Click **"Analyze Resume"**
    4. Wait 1-2 minutes for analysis
    
    ### 🔬 About This Tool
    
    Built with Mistral-7B language model for intelligent resume analysis.
    Identifies missing keywords and provides actionable suggestions.
    
    **First analysis takes longer** as the model loads into memory.
    
    ---
    
    💻 **Tech Stack:** PyTorch • Transformers • PEFT • Gradio  
    🔗 **Links:** [GitHub](#) | [LinkedIn](#) | [Portfolio](#)
    """)
    
    # Event
    analyze_btn.click(
        fn=analyze_resume,
        inputs=[resume_input, job_input],
        outputs=output
    )

print("\n🚀 Launching Gradio interface...")
demo.launch()