Update app.py

app.py

@@ -17,25 +17,18 @@ def load_model():
 
 model = load_model()
 
-def enhance_image(img):
-    img_array = np.array(img)
-    gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
-    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
-    enhanced = clahe.apply(gray)
-    return cv2.cvtColor(enhanced, cv2.COLOR_GRAY2RGB)
-
 uploaded_file = st.file_uploader("Téléchargez une image radiographique", type=["jpg", "jpeg", "png"])
 
 if uploaded_file:
+    # Convert uploaded file to PIL Image
     image = Image.open(uploaded_file).convert('RGB')
     if image.size[0] > 800:
         ratio = 800.0 / image.size[0]
         size = (800, int(image.size[1] * ratio))
         image = image.resize(size, Image.Resampling.LANCZOS)
     
-    enhanced_image = enhance_image(image)
-    
-    predictions = model(enhanced_image)
+    # Pass PIL Image directly to model
+    predictions = model(image)
     
     col1, col2 = st.columns(2)
     
@@ -43,26 +36,20 @@ if uploaded_file:
         st.image(image, caption="Image originale", use_container_width=True)
     
     with col2:
-        output_img = np.array(image).copy()
+        img_array = np.array(image)
         for pred in predictions:
             box = pred['box']
             score = pred['score']
-            
             x1, y1, x2, y2 = [int(i) for i in [box['xmin'], box['ymin'], box['xmax'], box['ymax']]]
-            cv2.rectangle(output_img, (x1, y1), (x2, y2), (255, 0, 0), 3)
-            
+            cv2.rectangle(img_array, (x1, y1), (x2, y2), (255, 0, 0), 3)
             text = f"Fracture: {score:.2f}"
-            cv2.putText(output_img, text, (x1, y1-10), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
+            cv2.putText(img_array, text, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
         
-        st.image(output_img, caption="Détection des fractures", use_container_width=True)
+        st.image(img_array, caption="Détection des fractures", use_container_width=True)
     
-    st.subheader("Résultats")
     if predictions:
+        st.subheader(f"🚨 {len(predictions)} fractures détectées:")
         for idx, pred in enumerate(predictions, 1):
-            st.warning(f"⚠️ Fracture {idx} détectée (Confiance: {pred['score']*100:.1f}%)")
+            st.warning(f"Fracture {idx}: Confiance {pred['score']*100:.1f}%")
     else:
-        st.warning("⚠️ Aucune fracture n'a été détectée avec certitude")
-        
-else:
-    st.info("Veuillez télécharger une image radiographique pour l'analyse.")
+        st.warning("⚠️ Aucune fracture détectée")