Update app.py

app.py

@@ -350,47 +350,48 @@ scaled_anchors = (
 
 uploaded_file = st.file_uploader("Chọn hình ảnh...", type=["jpg", "jpeg", "png"])
 # uploaded_file = '/home/ngocanh/Documents/final_thesis/code/dataset/10_10/base/images/test/000011.jpg'
-image = Image.open(uploaded_file)
-print("Thuc hien bien doi")
-
-#task 1
-file_path = f"2007_base_{base}_{new}_mAP_{base}_{new}.pth.tar"
-model = YOLOv3(num_classes=base).to(device)
-checkpoint = torch.load(file_path, map_location=device)
-model.load_state_dict(checkpoint["state_dict"])
-model.eval()
-image_1 = infer(model, image, 0.5, 0.5, scaled_anchors)
-
-#task 2
-file_path = f"2007_task2_{base}_{new}_mAP_{base}_{new}.pth.tar"
-model = YOLOv3(num_classes=all).to(device)
-checkpoint = torch.load(file_path, map_location=device)
-model.load_state_dict(checkpoint["state_dict"])
-model.eval()
-image_2 = infer(model, image, 0.5, 0.5, scaled_anchors)
-
-#ft
-file_path = f"2007_finetune_{base}_{new}_mAP_{base}_{new}.pth.tar"
-checkpoint = torch.load(file_path, map_location=device)
-model.load_state_dict(checkpoint["state_dict"])
-model.eval()
-image_3 = infer(model, image, 0.5, 0.5, scaled_anchors)
-# Streamlit App
-# Widget tải lên file ảnh
-
-# note = Image.open("note.png")
-# st.image(note, width=150)
-
-
-col1, col2, col3, col4 = st.columns(4)
-with col1:
-    st.image(image, caption="Ảnh đầu vào", use_column_width=True)
-with col2:
-    st.image(image_1, caption="Kết quả task 1", channels="BGR", use_column_width=True)
-with col3:
-    st.image(image_1, caption="Kết quả task 2 (no finetune)", channels="BGR", use_column_width=True)
-with col4:
-    st.image(image_1, caption="Kết quả task 2 (finetune)", channels="BGR", use_column_width=True)
+if uploaded_file is not None:sssssssssssssssssssssssssssssssssssssssssssssssss
+    image = Image.open(uploaded_file)
+    print("Thuc hien bien doi")
+    
+    #task 1
+    file_path = f"2007_base_{base}_{new}_mAP_{base}_{new}.pth.tar"
+    model = YOLOv3(num_classes=base).to(device)
+    checkpoint = torch.load(file_path, map_location=device)
+    model.load_state_dict(checkpoint["state_dict"])
+    model.eval()
+    image_1 = infer(model, image, 0.5, 0.5, scaled_anchors)
+    
+    #task 2
+    file_path = f"2007_task2_{base}_{new}_mAP_{base}_{new}.pth.tar"
+    model = YOLOv3(num_classes=all).to(device)
+    checkpoint = torch.load(file_path, map_location=device)
+    model.load_state_dict(checkpoint["state_dict"])
+    model.eval()
+    image_2 = infer(model, image, 0.5, 0.5, scaled_anchors)
+    
+    #ft
+    file_path = f"2007_finetune_{base}_{new}_mAP_{base}_{new}.pth.tar"
+    checkpoint = torch.load(file_path, map_location=device)
+    model.load_state_dict(checkpoint["state_dict"])
+    model.eval()
+    image_3 = infer(model, image, 0.5, 0.5, scaled_anchors)
+    # Streamlit App
+    # Widget tải lên file ảnh
+    
+    # note = Image.open("note.png")
+    # st.image(note, width=150)
+    
+    
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        st.image(image, caption="Ảnh đầu vào", use_column_width=True)
+    with col2:
+        st.image(image_1, caption="Kết quả task 1", channels="BGR", use_column_width=True)
+    with col3:
+        st.image(image_2, caption="Kết quả task 2 (no finetune)", channels="BGR", use_column_width=True)
+    with col4:
+        st.image(image_3, caption="Kết quả task 2 (finetune)", channels="BGR", use_column_width=True)
 
 
 # import cv2