from flask import Flask, render_template, request, jsonify, send_file from werkzeug.utils import secure_filename import pandas as pd import os import uuid import re from datetime import datetime # Try to import PyPDF2, if not available show error try: import PyPDF2 PDF_SUPPORT = True except ImportError: PDF_SUPPORT = False print("⚠️ PyPDF2 not installed. Run: pip install PyPDF2") app = Flask(__name__) # Configuration UPLOAD_FOLDER = 'uploads' EXCEL_FOLDER = 'excel_files' os.makedirs(UPLOAD_FOLDER, exist_ok=True) os.makedirs(EXCEL_FOLDER, exist_ok=True) app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER app.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024 # 50MB def allowed_file(filename): return '.' in filename and filename.rsplit('.', 1)[1].lower() == 'pdf' def extract_text_from_pdf(pdf_path): """Extract all text content from PDF file""" if not PDF_SUPPORT: return "PyPDF2 not installed. Please run: pip install PyPDF2" text_content = "" try: with open(pdf_path, 'rb') as file: pdf_reader = PyPDF2.PdfReader(file) num_pages = len(pdf_reader.pages) for page_num in range(num_pages): page = pdf_reader.pages[page_num] page_text = page.extract_text() if page_text: text_content += page_text + "\n\n" except Exception as e: text_content = f"Error extracting text: {str(e)}" return text_content if text_content else "No text could be extracted from this PDF." def extract_data_from_text(text): """Extract useful information from PDF text content""" data = { 'Extracted Text Preview': text[:1000] + "..." if len(text) > 1000 else text, 'Full Text Length': len(text), 'Word Count': len(text.split()), 'Line Count': len(text.split('\n')), 'Has Numbers': 'Yes' if re.search(r'\d', text) else 'No', 'Has Emails': 'Yes' if re.search(r'[\w\.-]+@[\w\.-]+\.\w+', text) else 'No', 'Has URLs': 'Yes' if re.search(r'https?://\S+', text) else 'No' } # Extract email addresses emails = re.findall(r'[\w\.-]+@[\w\.-]+\.\w+', text) data['Emails Found'] = ', '.join(list(set(emails))[:10]) if emails else 'None' data['Email Count'] = len(set(emails)) # Extract phone numbers phone_patterns = [ r'\+\d{1,3}[-.\s]?\d{3}[-.\s]?\d{3}[-.\s]?\d{4}', r'\d{3}[-.\s]?\d{3}[-.\s]?\d{4}', r'\(\d{3}\)\s?\d{3}[-.\s]?\d{4}', r'\d{4}[-.\s]?\d{7}', ] phones = [] for pattern in phone_patterns: phones.extend(re.findall(pattern, text)) data['Phone Numbers Found'] = ', '.join(list(set(phones))[:10]) if phones else 'None' data['Phone Count'] = len(set(phones)) # Extract numbers/amounts amounts = re.findall(r'\$\d+(?:,\d+)*(?:\.\d+)?|\d+(?:\.\d+)?\s?(?:USD|EUR|INR|PKR|Rupees|Rs\.?)', text, re.IGNORECASE) data['Amounts Found'] = ', '.join(amounts[:10]) if amounts else 'None' return data @app.route('/') def index(): return render_template('index.html') @app.route('/convert-pdf-to-excel', methods=['POST']) def convert_pdf_to_excel(): """Convert uploaded PDF to Excel file with extracted data""" try: # Check if file is uploaded if 'pdf_file' not in request.files: return jsonify({ 'success': False, 'message': 'No file uploaded! Please select a PDF file.' }), 400 uploaded_file = request.files['pdf_file'] if uploaded_file.filename == '': return jsonify({ 'success': False, 'message': 'No file selected! Please choose a PDF file.' }), 400 if not allowed_file(uploaded_file.filename): return jsonify({ 'success': False, 'message': 'Only PDF files are allowed! Please upload a .pdf file.' }), 400 # Save PDF file original_filename = secure_filename(uploaded_file.filename) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") unique_id = str(uuid.uuid4())[:6] pdf_filename = f"{timestamp}_{unique_id}_{original_filename}" pdf_path = os.path.join(app.config['UPLOAD_FOLDER'], pdf_filename) uploaded_file.save(pdf_path) # Extract text from PDF extracted_text = extract_text_from_pdf(pdf_path) # Extract data from text extracted_data = extract_data_from_text(extracted_text) # Create Excel file with multiple sheets excel_filename = f"PDF_Data_{timestamp}_{unique_id}.xlsx" excel_path = os.path.join(EXCEL_FOLDER, excel_filename) with pd.ExcelWriter(excel_path, engine='openpyxl') as writer: # Sheet 1: File Information df_file = pd.DataFrame([ { 'Original Filename': original_filename, 'Saved Filename': pdf_filename, 'File Size (KB)': round(os.path.getsize(pdf_path) / 1024, 2), 'Conversion Date': datetime.now().strftime("%Y-%m-%d %H:%M:%S"), 'Conversion ID': unique_id.upper() } ]) df_file.to_excel(writer, sheet_name='File Information', index=False) # Sheet 2: Statistics df_stats = pd.DataFrame([ { 'Total Characters': extracted_data['Full Text Length'], 'Total Words': extracted_data['Word Count'], 'Total Lines': extracted_data['Line Count'], 'Contains Numbers': extracted_data['Has Numbers'], 'Contains Emails': extracted_data['Has Emails'], 'Contains URLs': extracted_data['Has URLs'], 'Email Count': extracted_data['Email Count'], 'Phone Count': extracted_data['Phone Count'] } ]) df_stats.to_excel(writer, sheet_name='Statistics', index=False) # Sheet 3: Extracted Information df_extracted = pd.DataFrame([ { 'Email Addresses Found': extracted_data['Emails Found'], 'Phone Numbers Found': extracted_data['Phone Numbers Found'], 'Amounts/Values Found': extracted_data['Amounts Found'] } ]) df_extracted.to_excel(writer, sheet_name='Extracted Information', index=False) # Sheet 4: Full Text (split into chunks) text_chunks = [extracted_text[i:i+30000] for i in range(0, len(extracted_text), 30000)] df_text = pd.DataFrame({ 'Chunk Number': range(1, len(text_chunks) + 1), 'Text Content': text_chunks }) if text_chunks else pd.DataFrame({'Text Content': ['No text extracted']}) df_text.to_excel(writer, sheet_name='Full Extracted Text', index=False) # Sheet 5: Preview df_preview = pd.DataFrame([ {'Preview (First 1000 chars)': extracted_data['Extracted Text Preview']} ]) df_preview.to_excel(writer, sheet_name='Text Preview', index=False) # Return the Excel file return send_file( excel_path, as_attachment=True, download_name=f"PDF_to_Excel_{original_filename.replace('.pdf', '')}_{timestamp}.xlsx", mimetype='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet' ) except Exception as e: print(f"Error: {str(e)}") return jsonify({ 'success': False, 'message': f'Conversion failed: {str(e)}' }), 500 @app.route('/preview-pdf', methods=['POST']) def preview_pdf(): """Get preview data from PDF without downloading Excel""" try: if 'pdf_file' not in request.files: return jsonify({'success': False, 'message': 'No file uploaded'}), 400 uploaded_file = request.files['pdf_file'] if uploaded_file.filename == '' or not allowed_file(uploaded_file.filename): return jsonify({'success': False, 'message': 'Invalid PDF file'}), 400 original_filename = secure_filename(uploaded_file.filename) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") unique_id = str(uuid.uuid4())[:6] pdf_filename = f"{timestamp}_{unique_id}_{original_filename}" pdf_path = os.path.join(app.config['UPLOAD_FOLDER'], pdf_filename) uploaded_file.save(pdf_path) extracted_text = extract_text_from_pdf(pdf_path) extracted_data = extract_data_from_text(extracted_text) # Clean up temp file after preview try: os.remove(pdf_path) except: pass return jsonify({ 'success': True, 'message': 'PDF analyzed successfully!', 'filename': original_filename, 'stats': { 'characters': extracted_data['Full Text Length'], 'words': extracted_data['Word Count'], 'lines': extracted_data['Line Count'], 'emails': extracted_data['Email Count'], 'phones': extracted_data['Phone Count'] }, 'found_data': { 'emails': extracted_data['Emails Found'], 'phones': extracted_data['Phone Numbers Found'], 'amounts': extracted_data['Amounts Found'] }, 'preview': extracted_text[:500] + "..." if len(extracted_text) > 500 else extracted_text }) except Exception as e: print(f"Preview error: {str(e)}") return jsonify({'success': False, 'message': str(e)}), 500 if __name__ == '__main__': print("\n" + "="*60) print("📄 PDF TO EXCEL CONVERTER") print("📍 User uploads PDF -> Click Convert -> Get Excel file") print("🌐 Open in browser: http://127.0.0.1:5000") print("="*60 + "\n") app.run(debug=True, host='127.0.0.1', port=5000)