Add gradio

app.py

@@ -0,0 +1,103 @@
+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()

requirements.txt

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+torch==2.3.1
+transformers==4.41.2
+gradio==4.37.2
+numpy==1.23.5
+scipy==1.11.3
+black
+pytest
+scikit-learn
+sentencepiece==0.2.0
+PyMuPDF==1.24.5  
+pytesseract==0.3.10
+pillow==10.3.0
+protobuf