from flask import Flask, render_template,request, redirect,url_for, jsonify , session from helper_functions import predict_class , prepare_text , inference , predict , align_predictions_with_sentences , load_models import fitz # PyMuPDF import os, shutil import torch import tempfile from pydub import AudioSegment import logging app = Flask(__name__) app.config['UPLOAD_FOLDER'] = 'static/uploads' # Global variables for models global_model = None global_neptune = None global_tokenizer = None global_pipe = None def init_app(): global global_model, global_neptune, global_pipe print("Loading models...") global_model, global_neptune, global_pipe = load_models() print("Models loaded successfully!") init_app() @app.route("/") def home(): predict_class = "" class_probabilities = dict() chart_data = dict() return render_template('pdf.html', class_probabilities= class_probabilities, predicted_class=predict_class,chart_data = chart_data) @app.route('/pdf') def pdf(): predict_class = "" class_probabilities = dict() chart_data = dict() return render_template('pdf.html', class_probabilities= class_probabilities, predicted_class=predict_class,chart_data = chart_data) @app.route('/pdf/upload' , methods = ['POST']) def treatment(): global global_model, global_tokenizer if request.method == 'POST' : # Récupérer le fichier PDF de la requête file = request.files['file'] filename = file.filename # Enregistrer le fichier dans le répertoire de téléchargement filepath = app.config['UPLOAD_FOLDER'] + "/" + filename file.save(filepath) # Ouvrir le fichier PDF pdf_document = fitz.open(filepath) # Initialiser une variable pour stocker le texte extrait extracted_text = "" # Boucler à travers chaque page pour extraire le texte for page_num in range(len(pdf_document)): # Récupérer l'objet de la page page = pdf_document.load_page(page_num) # Extraire le texte de la page page_text = page.get_text() # Ajouter le texte de la page à la variable d'extraction extracted_text += f"\nPage {page_num + 1}:\n{page_text}" # Fermer le fichier PDF pdf_document.close() # Prepare data for the chart predicted_class , class_probabilities = predict_class([extracted_text] , global_model) chart_data = { 'datasets': [{ 'data': list(class_probabilities.values()), 'backgroundColor': [color[2] for color in class_probabilities.keys()], 'borderColor': [color[2] for color in class_probabilities.keys()] }], 'labels': [label[0] for label in class_probabilities.keys()] } print(predict_class) print(chart_data) # clear the uploads folder for filename in os.listdir(app.config['UPLOAD_FOLDER']): file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) try: if os.path.isfile(file_path) or os.path.islink(file_path): os.unlink(file_path) elif os.path.isdir(file_path): shutil.rmtree(file_path) except Exception as e: print('Failed to delete %s. Reason: %s' % (file_path, e)) return render_template('pdf.html',extracted_text = extracted_text, class_probabilities=class_probabilities, predicted_class=predicted_class, chart_data = chart_data) return render_template('pdf.html') ## Sentence @app.route('/sentence' , methods = ['GET' , 'POST']) def sentence(): global global_model, global_tokenizer if request.method == 'POST': # Get the form data text = [request.form['text']] predicted_class , class_probabilities = predict_class(text , global_model) # Prepare data for the chart chart_data = { 'datasets': [{ 'data': list(class_probabilities.values()), 'backgroundColor': [color[2 ] for color in class_probabilities.keys()], 'borderColor': [color[2] for color in class_probabilities.keys()] }], 'labels': [label[0] for label in class_probabilities.keys()] } print(chart_data) # clear the uploads folder for filename in os.listdir(app.config['UPLOAD_FOLDER']): file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename) try: if os.path.isfile(file_path) or os.path.islink(file_path): os.unlink(file_path) elif os.path.isdir(file_path): shutil.rmtree(file_path) except Exception as e: print('Failed to delete %s. Reason: %s' % (file_path, e)) return render_template('response_sentence.html', text=text, class_probabilities=class_probabilities, predicted_class=predicted_class,chart_data = chart_data) # Render the initial form page return render_template('sentence.html') ## Voice @app.route("/voice_backup") def slu_backup(): input_file = "static/uploads/2022.jep-architectures-neuronales.pdf" # Ouvrir le fichier PDF pdf_document = fitz.open(input_file) # Initialiser une variable pour stocker le texte extrait extracted_text = "" # Boucler à travers chaque page pour extraire le texte for page_num in range(len(pdf_document)): # Récupérer l'objet de la page page = pdf_document.load_page(page_num) # Extraire le texte de la page page_text = page.get_text() # Ajouter le texte de la page à la variable d'extraction extracted_text += f"\nPage {page_num + 1}:\n{page_text}" # Fermer le fichier PDF pdf_document.close() # Prepare data for the chart inference_batch, sentences = inference(extracted_text) predictions = predict(inference_batch) sentences_prediction = align_predictions_with_sentences(sentences, predictions) predicted_class , class_probabilities = predict_class([extracted_text] , global_model) chart_data = { 'datasets': [{ 'data': list(class_probabilities.values()), 'backgroundColor': [color[2 ] for color in class_probabilities.keys()], 'borderColor': [color[2] for color in class_probabilities.keys()] }], 'labels': [label[0] for label in class_probabilities.keys()] } print(class_probabilities) print(chart_data) print(sentences_prediction) return render_template('voice_backup.html',extracted_text = extracted_text, class_probabilities=class_probabilities, predicted_class=predicted_class, chart_data = chart_data, sentences_prediction = sentences_prediction) logging.basicConfig(level=logging.DEBUG) @app.route("/voice", methods=['GET', 'POST']) def slu(): global global_neptune, global_pipe, global_model if request.method == 'POST': logging.debug("Received POST request") audio_file = request.files.get('audio') if audio_file: logging.debug(f"Received audio file: {audio_file.filename}") # Save audio data to a temporary file with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_audio: audio_file.save(temp_audio) temp_audio_path = temp_audio.name logging.debug(f"Saved audio to temporary file: {temp_audio_path}") try: # Transcribe audio using Whisper result = global_pipe(temp_audio_path) extracted_text = result["text"] logging.debug(f"Transcribed text: {extracted_text}") # Process the transcribed text inference_batch, sentences = inference(extracted_text) predictions = predict(inference_batch, global_neptune) sentences_prediction = align_predictions_with_sentences(sentences, predictions) predicted_class, class_probabilities = predict_class([extracted_text], global_model) chart_data = { 'datasets': [{ 'data': list(class_probabilities.values()), 'backgroundColor': [color[2] for color in class_probabilities.keys()], 'borderColor': [color[2] for color in class_probabilities.keys()] }], 'labels': [label[0] for label in class_probabilities.keys()] } response_data = { 'extracted_text': extracted_text, 'class_probabilities' : class_probabilities, 'predicted_class': predicted_class, 'chart_data': chart_data, 'sentences_prediction': sentences_prediction } logging.debug(f"Prepared response data: {response_data}") return render_template('voice.html', class_probabilities= class_probabilities, predicted_class= predicted_class, chart_data= chart_data, sentences_prediction=sentences_prediction) except Exception as e: logging.error(f"Error processing audio: {str(e)}") return jsonify({'error': str(e)}), 500 finally: # Remove temporary file os.unlink(temp_audio_path) else: logging.error("No audio file received") return jsonify({'error': 'No audio file received'}), 400 # For GET request logging.debug("Received GET request") return render_template('voice.html', class_probabilities={}, predicted_class=[""], chart_data={}, sentences_prediction={}) if __name__ == '__main__': app.run(debug=True)