Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import requests
-import json
+import os
 from PIL import Image
 import io
 import base64
@@ -8,93 +8,58 @@ import base64
 class FashionClassifier:
     def __init__(self):
         self.api_url = "https://api.marqo.ai/classify"
-        # Vous devrez gérer les clés API via les secrets Hugging Face
-        self.api_key = "your_marqo_api_key"  # À remplacer par les secrets
+        self.api_key = os.getenv("MARQO_API_KEY")  # Clé récupérée des secrets
     
     def classify_image(self, image, max_categories=5, confidence_threshold=0.3):
-        """Classifie une image uploadée"""
-        try:
-            # Convertir l'image en base64
-            buffered = io.BytesIO()
-            image.save(buffered, format="JPEG")
-            img_str = base64.b64encode(buffered.getvalue()).decode()
-            
-            headers = {
-                "Content-Type": "application/json",
-                "Authorization": f"Bearer {self.api_key}"
-            }
-            
-            payload = {
-                "model": "Marqo/Marqo-FashionSigLIP-Classification",
-                "image_data": img_str,
-                "parameters": {
-                    "max_categories": max_categories,
-                    "confidence_threshold": confidence_threshold
-                }
+        # Convertir image en base64
+        buffered = io.BytesIO()
+        image.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}"
+        }
+        
+        payload = {
+            "model": "Marqo/Marqo-FashionSigLIP-Classification",
+            "image_data": img_str,
+            "parameters": {
+                "max_categories": max_categories,
+                "confidence_threshold": confidence_threshold
             }
-            
-            response = requests.post(self.api_url, headers=headers, json=payload)
-            
-            if response.status_code == 200:
-                return response.json()
-            else:
-                return {"error": f"Erreur API: {response.status_code}", "details": response.text}
-                
-        except Exception as e:
-            return {"error": f"Erreur: {str(e)}"}
+        }
+        
+        response = requests.post(self.api_url, headers=headers, json=payload)
+        return response.json()
 
-# Initialiser le classifieur
 classifier = FashionClassifier()
 
 def process_image(image, max_categories=5, confidence=0.3):
-    """Fonction de traitement pour Gradio"""
     result = classifier.classify_image(image, max_categories, confidence)
     
-    if "error" in result:
-        return result["error"]
-    
-    # Formater les résultats
     if "predictions" in result:
-        output = "## Résultats de classification:\n\n"
-        for i, pred in enumerate(result["predictions"]):
-            output += f"{i+1}. **{pred['label']}** - {pred['score']*100:.2f}%\n"
-        
-        if "processing_time" in result:
-            output += f"\n⏱️ Temps de traitement: {result['processing_time']}s"
-        
+        output = "## Résultats :\n\n"
+        for pred in result["predictions"]:
+            output += f"- **{pred['label']}** : {pred['score']*100:.1f}%\n"
         return output
     else:
-        return "Aucune prédiction trouvée"
+        return "Erreur de classification"
 
-# Interface Gradio
-with gr.Blocks(title="Classificateur de Mode Marqo") as demo:
-    gr.Markdown("# 🎨 Classificateur de Vêtements Marqo")
-    gr.Markdown("Uploadez une image de vêtement pour la classifier")
-    
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎨 Classificateur de Vêtements")
     with gr.Row():
-        with gr.Column():
-            image_input = gr.Image(type="pil", label="Image à classifier")
-            max_categories = gr.Slider(1, 10, value=5, label="Nombre max de catégories")
-            confidence = gr.Slider(0.1, 1.0, value=0.3, label="Seuil de confiance")
-            submit_btn = gr.Button("Classifier l'image")
-        
-        with gr.Column():
-            output_text = gr.Markdown(label="Résultats")
+        image_input = gr.Image(type="pil")
+        output_text = gr.Markdown()
+    
+    max_categories = gr.Slider(1, 10, value=5)
+    confidence = gr.Slider(0.1, 1.0, value=0.3)
+    submit_btn = gr.Button("Classifier")
     
     submit_btn.click(
         fn=process_image,
         inputs=[image_input, max_categories, confidence],
         outputs=output_text
     )
-    
-    # Exemples
-    gr.Examples(
-        examples=[
-            ["https://example.com/image1.jpg", 5, 0.3],
-            ["https://example.com/image2.jpg", 3, 0.4]
-        ],
-        inputs=[image_input, max_categories, confidence]
-    )
 
-if __name__ == "__main__":
-    demo.launch(share=True)
+demo.launch()