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from flask import Flask, render_template, request, redirect, url_for, flash, session, send_from_directory |
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import os |
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import logging |
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from utility.utils import extract_text_from_images, Data_Extractor, json_to_llm_str, process_extracted_text, process_resume_data |
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from backup.backup import NER_Model |
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from paddleocr import PaddleOCR |
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logging.basicConfig( |
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level=logging.INFO, |
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handlers=[ |
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logging.StreamHandler() |
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] |
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) |
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app = Flask(__name__) |
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app.secret_key = 'your_secret_key' |
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app.config['UPLOAD_FOLDER'] = 'uploads/' |
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app.config['RESULT_FOLDER'] = 'uploads/' |
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UPLOAD_FOLDER = 'static/uploads/' |
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RESULT_FOLDER = 'static/results/' |
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os.makedirs(UPLOAD_FOLDER, exist_ok=True) |
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os.makedirs(RESULT_FOLDER, exist_ok=True) |
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if not os.path.exists(app.config['UPLOAD_FOLDER']): |
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os.makedirs(app.config['UPLOAD_FOLDER']) |
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if not os.path.exists(app.config['RESULT_FOLDER']): |
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os.makedirs(app.config['RESULT_FOLDER']) |
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os.environ['PADDLEOCR_HOME'] = '/tmp/.paddleocr' |
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if not os.path.exists('/tmp/.paddleocr'): |
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os.makedirs('/tmp/.paddleocr', exist_ok=True) |
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logging.info("Created PaddleOCR home directory.") |
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else: |
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logging.info("PaddleOCR home directory exists.") |
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@app.route('/') |
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def index(): |
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uploaded_files = session.get('uploaded_files', []) |
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logging.info(f"Accessed index page, uploaded files: {uploaded_files}") |
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return render_template('index.html', uploaded_files=uploaded_files) |
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@app.route('/upload', methods=['POST']) |
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def upload_file(): |
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if 'files' not in request.files: |
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flash('No file part') |
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logging.warning("No file part found in the request") |
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return redirect(request.url) |
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files = request.files.getlist('files') |
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if not files or all(file.filename == '' for file in files): |
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flash('No selected files') |
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logging.warning("No files selected for upload") |
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return redirect(request.url) |
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uploaded_files = session.get('uploaded_files', []) |
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for file in files: |
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if file: |
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filename = file.filename |
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file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) |
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file.save(file_path) |
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uploaded_files.append(filename) |
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logging.info(f"Uploaded file: {filename} at {file_path}") |
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session['uploaded_files'] = uploaded_files |
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flash('Files successfully uploaded') |
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logging.info(f"Files successfully uploaded: {uploaded_files}") |
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return redirect(url_for('index')) |
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@app.route('/remove_file') |
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def remove_file(): |
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uploaded_files = session.get('uploaded_files', []) |
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for filename in uploaded_files: |
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file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) |
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if os.path.exists(file_path): |
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os.remove(file_path) |
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logging.info(f"Removed file: {filename}") |
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else: |
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logging.warning(f"File not found for removal: {file_path}") |
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session.pop('uploaded_files', None) |
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flash('Files successfully removed') |
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logging.info("All uploaded files removed") |
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return redirect(url_for('index')) |
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@app.route('/process', methods=['POST']) |
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def process_file(): |
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uploaded_files = session.get('uploaded_files', []) |
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if not uploaded_files: |
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flash('No files selected for processing') |
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logging.warning("No files selected for processing") |
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return redirect(url_for('index')) |
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file_paths = [os.path.join(app.config['UPLOAD_FOLDER'], filename) for filename in uploaded_files] |
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logging.info(f"Processing files: {file_paths}") |
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extracted_text = {} |
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processed_Img = {} |
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try: |
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extracted_text, processed_Img = extract_text_from_images(file_paths) |
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logging.info(f"Extracted text: {extracted_text}") |
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logging.info(f"Processed images: {processed_Img}") |
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llmText = json_to_llm_str(extracted_text) |
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logging.info(f"LLM text: {llmText}") |
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LLMdata = Data_Extractor(llmText) |
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logging.info(f"LLM data: {LLMdata}") |
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except Exception as e: |
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logging.error(f"Error during LLM processing: {e}") |
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logging.info("Running backup model...") |
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LLMdata = {} |
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extracted_text, processed_Img = extract_text_from_images(file_paths) |
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logging.info(f"Extracted text(Backup): {extracted_text}") |
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logging.info(f"Processed images(Backup): {processed_Img}") |
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if extracted_text: |
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text = json_to_llm_str(extracted_text) |
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LLMdata = NER_Model(text) |
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logging.info(f"NER model data: {LLMdata}") |
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else: |
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logging.warning("No extracted text available for backup model") |
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cont_data = process_extracted_text(extracted_text) |
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logging.info(f"Contextual data: {cont_data}") |
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processed_data = process_resume_data(LLMdata, cont_data, extracted_text) |
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logging.info(f"Processed data: {processed_data}") |
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session['processed_data'] = processed_data |
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session['processed_Img'] = processed_Img |
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flash('Data processed and analyzed successfully') |
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logging.info("Data processed and analyzed successfully") |
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return redirect(url_for('result')) |
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@app.route('/result') |
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def result(): |
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processed_data = session.get('processed_data', {}) |
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processed_Img = session.get('processed_Img', {}) |
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logging.info(f"Displaying results: Data - {processed_data}, Images - {processed_Img}") |
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return render_template('result.html', data=processed_data, Img=processed_Img) |
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@app.route('/uploads/<filename>') |
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def uploaded_file(filename): |
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logging.info(f"Serving file: {filename}") |
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return send_from_directory(app.config['UPLOAD_FOLDER'], filename) |
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if __name__ == '__main__': |
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logging.info("Starting Flask app") |
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app.run(debug=True) |
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