Update app.py

app.py

@@ -46,6 +46,18 @@ model = models[model_name]
 file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
 # file ='hmnist_28_28_RGB.csv'
 
+
+
+# uploaded_file = st.file_uploader("Choose a image file", type="jpg")
+
+if uploaded_file is not None:
+    # Convert the file to an opencv image.
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    opencv_image = cv2.imdecode(file_bytes, 1)
+
+    st.image(opencv_image, channels="BGR")
+    
+
 classes = {4: ('nv', ' melanocytic nevi'), 6: ('mel', 'melanoma'),
            2 :('bkl', 'benign keratosis-like lesions'), 1:('bcc' , ' basal cell carcinoma'),
            5: ('vasc', ' pyogenic granulomas and hemorrhage'),
@@ -53,17 +65,22 @@ classes = {4: ('nv', ' melanocytic nevi'), 6: ('mel', 'melanoma'),
            3: ('df', 'dermatofibroma')}
 
 
-if file is not None:   
-    temp_dir = tempfile.TemporaryDirectory()
-    temp_file_path = temp_dir.name + "/" + file.name
+if file is not None:
+
+    file_bytes = np.asarray(bytearray(file.read()), dtype=np.uint8)
+    opencv_image = cv2.imdecode(file_bytes, 1)
+    
+    
+    # temp_dir = tempfile.TemporaryDirectory()
+    # temp_file_path = temp_dir.name + "/" + file.name
     
-    # Save the uploaded file to the temporary directory
-    with open(temp_file_path, "wb") as f:
-        f.write(file.read())
+    # # Save the uploaded file to the temporary directory
+    # with open(temp_file_path, "wb") as f:
+    #     f.write(file.read())
         
-    img = cv2.imread(file)
+    # img = cv2.imread(file)
     # cv2_imshow(img)
-    img1 = cv2.resize(img, (28, 28))
+    img1 = cv2.resize(opencv_image, (28, 28))
     result = model.predict(img1.reshape(1, 28, 28, 3))
     max_prob = max(result[0])
     class_ind = list(result[0]).index(max_prob)
@@ -87,7 +104,7 @@ if file is not None:
     col1, col2 = st.columns(2)
     with col1:
         st.header("Input Image")
-        st.image(img)
+        st.image(opencv_image, channels="BGR")
     with col2:
         st.header("Prediction")
         st.write(class_name)