app.py

import os
from flask import Flask, request, jsonify
from transformers import pipeline
from werkzeug.utils import secure_filename
from pdf2image import convert_from_path
import pytesseract
from PIL import Image

# Initialize Flask app
app = Flask(__name__)

# Set upload folder
UPLOAD_FOLDER = 'uploads'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER

# Load AI Pipelines
ocr_pipeline = pipeline("image-to-text", model="microsoft/trocr-small-printed")  # OCR Model
text_gen_pipeline = pipeline("text-generation", model="distilbert/distilgpt2")   # Text Generation Model

# Function to extract text from a PDF resume
def extract_text_from_pdf(pdf_path):
    images = convert_from_path(pdf_path)
    extracted_text = ""
    
    for img in images:
        text = pytesseract.image_to_string(img)  # OCR extraction
        extracted_text += text + "\n"
    
    return extracted_text.strip()

# Route: Upload Resume & Generate Report
@app.route('/upload', methods=['POST'])
def upload_resume():
    if 'file' not in request.files:
        return jsonify({"error": "No file uploaded"}), 400
    
    file = request.files['file']
    if file.filename == '':
        return jsonify({"error": "No file selected"}), 400
    
    # Save uploaded file
    filename = secure_filename(file.filename)
    file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
    file.save(file_path)

    # Extract text from PDF
    extracted_text = extract_text_from_pdf(file_path)

    # Generate AI evaluation
    prompt = f"Candidate Resume: {extracted_text}\n\nEvaluate the suitability of this candidate for a software engineering role at Google."
    ai_evaluation = text_gen_pipeline(prompt, max_length=150, num_return_sequences=1)[0]["generated_text"]

    # Return response
    response = {
        "resume_text": extracted_text[:1000],  # Limit to 1000 chars for display
        "ai_evaluation": ai_evaluation
    }
    
    return jsonify(response)

# Run Flask App
if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)