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| import gradio as gr | |
| from transformers import pipeline | |
| import pytesseract | |
| from PIL import Image, UnidentifiedImageError | |
| import re | |
| import os | |
| import logging | |
| # Configurer les répertoires de cache | |
| os.environ['TRANSFORMERS_CACHE'] = '/app/.cache' | |
| os.environ['HF_HOME'] = '/app/.cache' | |
| # Configurer les logs | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Initialiser les pipelines | |
| summarize = pipeline('summarization', model="facebook/bart-large-cnn") | |
| pipe = pipeline("summarization", model="plguillou/t5-base-fr-sum-cnndm") | |
| classify_zero_shot = pipeline("zero-shot-classification", model="facebook/bart-large-mnli") | |
| # Fonction de résumé de texte avec classification | |
| def summarize_text(text): | |
| if text.strip() == "": | |
| return "Veuillez entrer un texte", {} | |
| preprocessing_text = re.sub(r'\s+', ' ', text).strip() | |
| summary = pipe(preprocessing_text, do_sample=False) | |
| summary_text = summary[0].get('summary_text') | |
| logger.info(f"[INFO] Input data: {preprocessing_text}") | |
| logger.info(f"[INFO] Summary: {summary_text}") | |
| result = classify_zero_shot( | |
| summary_text, | |
| candidate_labels=["En Cours", "Non traiter", "Terminer"], | |
| hypothesis_template="Cet Résumé est sur {}." | |
| ) | |
| scores = {label: float(score) for label, score in zip(result['labels'], result['scores'])} | |
| return summary_text, scores | |
| # Fonction de chargement d'image | |
| def image_load(image): | |
| try: | |
| if image is None: | |
| return "Aucune image fournie", {} | |
| raw_text = pytesseract.image_to_string(image, lang='fra') | |
| preprocessing = re.sub(r'\s+', ' ', raw_text).strip() | |
| text_summary = pipe(preprocessing, do_sample=False) | |
| summary_text_from_image = text_summary[0].get('summary_text') | |
| result = classify_zero_shot( | |
| summary_text_from_image, | |
| candidate_labels=["En Cours", "Non traiter", "Terminer"], | |
| hypothesis_template="Cet Résumé est sur {}." | |
| ) | |
| scores = {label: float(score) for label, score in zip(result['labels'], result['scores'])} | |
| logger.info(f"[INFO] Input data: {preprocessing}") | |
| logger.info(f"[INFO] Summary: {result}") | |
| return summary_text_from_image,scores | |
| except UnidentifiedImageError: | |
| return "Impossible de charger l'image", {} | |
| except Exception as e: | |
| logger.error(f"Error processing image: {e}") | |
| return str(e), {} | |
| # Fonction de gestion des entrées | |
| def handle_input(text_input, image_input, mode): | |
| if mode == "Texte": | |
| return summarize_text(text_input) | |
| elif mode == "Image": | |
| return image_load(image_input) | |
| else: | |
| return "Sélectionnez une option valide", {} | |
| # Interface Gradio | |
| with gr.Blocks() as iface: | |
| gr.Markdown("## Sélectionnez une option") | |
| with gr.Row(): | |
| with gr.Column(): | |
| mode = gr.Dropdown(choices=["Texte", "Image"], label="Resumé Texte ou Image",info="Selectionner une options") | |
| text_input = gr.Textbox(lines=4,label="Entrée de texte") | |
| image_input = gr.Image(label="Téléverser une image", type="pil") | |
| submit_btn = gr.Button("Soumettre") | |
| with gr.Column(): | |
| output_summary = gr.Textbox(label="Résumé") | |
| output_classification = gr.Label(label="Classification") | |
| def update_inputs(mode_select): | |
| if mode_select == "Texte": | |
| return gr.update(visible=True), gr.update(visible=False) | |
| elif mode_select == "Image": | |
| return gr.update(visible=False), gr.update(visible=True) | |
| logger.info(f"[INFO] input mode: {update_inputs}") | |
| mode.change(fn=update_inputs, inputs=mode, outputs=[text_input, image_input]) | |
| submit_btn.click(fn=handle_input, inputs=[text_input, image_input, mode], outputs=[output_summary, output_classification]) | |
| if __name__ == "__main__": | |
| iface.launch() | |