Update app.py

app.py

@@ -1,55 +1,69 @@
 import streamlit as st
-from transformers import pipeline
+from transformers import AutoModelForObjectDetection
+import torch
 from PIL import Image
 import numpy as np
 import cv2
 
-st.set_page_config(page_title="Détection de fractures osseuses")
-st.title("Détection de fractures osseuses par rayons X")
+st.set_page_config(page_title="Détection de nodules pulmonaires")
+st.title("Détection de nodules pulmonaires sur images scanner")
 
 @st.cache_resource
 def load_model():
-    return pipeline(
-        "object-detection",
-        model="anirban22/detr-resnet-50-med_fracture",
-        threshold=0.1
-    )
+    model = AutoModelForObjectDetection.from_pretrained("monai-test/lung_nodule_ct_detection")
+    model.eval()
+    return model
 
-model = load_model()
+def process_image(image):
+    # Convertir en niveau de gris
+    img_array = np.array(image.convert('L'))
+    # Normaliser
+    normalized = (img_array - img_array.min()) / (img_array.max() - img_array.min())
+    # Redimensionner
+    resized = cv2.resize(normalized, (512, 512))
+    # Préparer pour PyTorch
+    tensor = torch.FloatTensor(resized).unsqueeze(0).unsqueeze(0)
+    return tensor
 
-uploaded_file = st.file_uploader("Téléchargez une image radiographique", type=["jpg", "jpeg", "png"])
+try:
+    model = load_model()
 
-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)
-    
-    # Pass PIL Image directly to model
-    predictions = model(image)
-    
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.image(image, caption="Image originale", use_container_width=True)
-    
-    with col2:
-        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(img_array, (x1, y1), (x2, y2), (255, 0, 0), 3)
-            text = f"Fracture: {score:.2f}"
-            cv2.putText(img_array, text, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
+    uploaded_file = st.file_uploader("Téléchargez une image scanner", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file:
+        image = Image.open(uploaded_file)
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.image(image, caption="Image originale", use_container_width=True)
         
-        st.image(img_array, caption="Détection des fractures", use_container_width=True)
-    
-    if predictions:
-        st.subheader(f"🚨 {len(predictions)} fractures détectées:")
-        for idx, pred in enumerate(predictions, 1):
-            st.warning(f"Fracture {idx}: Confiance {pred['score']*100:.1f}%")
-    else:
-        st.warning("⚠️ Aucune fracture détectée")
+        with col2:
+            with torch.no_grad():
+                input_tensor = process_image(image)
+                predictions = model(input_tensor)
+                
+            # Visualisation
+            img_array = np.array(image)
+            for pred in predictions:
+                if pred['score'] > 0.5:
+                    box = pred['box']
+                    x1, y1, x2, y2 = map(int, [box['xmin'], box['ymin'], box['xmax'], box['ymax']])
+                    cv2.rectangle(img_array, (x1, y1), (x2, y2), (255, 0, 0), 2)
+                    text = f"Nodule: {pred['score']:.2f}"
+                    cv2.putText(img_array, text, (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+            
+            st.image(img_array, caption="Détections", use_container_width=True)
+        
+        # Résultats
+        if len(predictions) > 0:
+            st.warning(f"⚠️ {len(predictions)} nodules détectés")
+            for i, pred in enumerate(predictions, 1):
+                if pred['score'] > 0.5:
+                    st.write(f"Nodule {i}: Confiance {pred['score']:.1%}")
+        else:
+            st.success("✅ Aucun nodule détecté")
+
+except Exception as e:
+    st.error(f"Erreur lors du chargement du modèle: {str(e)}")
+    st.info("Veuillez vérifier que le modèle est correctement configuré sur Hugging Face.")