Update app.py

app.py

@@ -22,9 +22,9 @@ st.markdown("""
     }
 
     .stImage > img {
-        width: 70% !important;
+        width: 50% !important;
         height: auto !important;
-        max-height: 150px !important;
+        max-height: 120px !important;
         object-fit: contain !important;
     }
 
@@ -36,6 +36,14 @@ st.markdown("""
         font-size: 0.8rem !important;
         margin: 0.2rem 0 !important;
     }
+
+    .center-container {
+        display: flex;
+        flex-direction: column;
+        align-items: center;
+        justify-content: center;
+        height: 100%;
+    }
 </style>
 """, unsafe_allow_html=True)
 
@@ -88,28 +96,30 @@ def draw_boxes(image, predictions):
 def main():
     models = load_models()
 
-    # Disposition en deux colonnes principales
-    col1, col2 = st.columns([1, 2])
+    if "uploaded" not in st.session_state:
+        st.session_state["uploaded"] = False
 
-    with col1:
-        st.markdown("### 📤 Röntgenbild Upload")
+    if not st.session_state["uploaded"]:
+        st.markdown("""
+        <div class="center-container">
+            <h2>📤 Röntgenbild Hochladen</h2>
+            <p>Bitte laden Sie ein Röntgenbild hoch, um die Analyse zu starten.</p>
+        </div>
+        """, unsafe_allow_html=True)
         uploaded_file = st.file_uploader("", type=['png', 'jpg', 'jpeg'])
 
         if uploaded_file:
-            conf_threshold = st.slider(
-                "Konfidenzschwelle",
-                min_value=0.0, max_value=1.0,
-                value=0.60, step=0.05
-            )
-
-    with col2:
-        if uploaded_file:
-            image = Image.open(uploaded_file)
+            st.session_state["uploaded"] = True
+            st.session_state["file"] = uploaded_file
+    else:
+        uploaded_file = st.session_state["file"]
+        col1, col2, col3 = st.columns([1, 1.5, 1])
 
-            # Toujours afficher les résultats des autres modèles
+        with col1:
             st.markdown("### 🎯 KI-Analyse")
 
             st.markdown("**🛡️ Der KnochenWächter**")
+            image = Image.open(uploaded_file)
             predictions_wachter = models["KnochenWächter"](image)
             for pred in predictions_wachter:
                 score_color = "#22c55e" if pred['score'] > 0.7 else "#eab308"
@@ -133,18 +143,18 @@ def main():
                     </div>
                 """, unsafe_allow_html=True)
 
-            # Analyse avec KnochenAuge (localisation)
-            predictions_auge = models["KnochenAuge"](image)
-            filtered_preds = [p for p in predictions_auge if p['score'] >= conf_threshold]
+        with col2:
+            st.image(image, use_column_width=True)
 
-            if filtered_preds:
-                st.markdown("#### 👁️ Das KnochenAuge - Lokalisation")
+        predictions_auge = models["KnochenAuge"](image)
+        filtered_preds = [p for p in predictions_auge if p['score'] >= 0.6]
+
+        if filtered_preds:
+            with col3:
+                st.markdown("### 👁️ Das KnochenAuge - Lokalisation")
                 result_image = image.copy()
                 result_image = draw_boxes(result_image, filtered_preds)
-                st.image(result_image, use_container_width=True)
-
-        else:
-            st.info("Bitte laden Sie ein Röntgenbild hoch (JPEG, PNG)")
+                st.image(result_image, use_column_width=True)
 
 if __name__ == "__main__":
     main()