Update app.py

app.py

@@ -47,8 +47,9 @@ def crop_imgs(set_name, add_pixels_value=0):
     return np.array(set_new)
 
 # Function to preprocess the image
-def preprocess_image(img_path):
-    img = image.load_img(img_path, target_size=(200, 200))
+def preprocess_image(img_array):
+    img_array = cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB)
+    img = image.array_to_img(img_array, target_size=(200, 200))
     img_array = image.img_to_array(img)
     img_array = np.expand_dims(img_array, axis=0)
     img_array /= 255.0  # Normalize the image
@@ -87,18 +88,24 @@ def main():
         st.write("")
         st.write("Classifying...")
 
-        # Read the contents of the uploaded file
+        # Read the contents of the uploaded file or the input image
         file_contents = uploaded_file.read()
+        uploaded_array = np.asarray(bytearray(file_contents), dtype=np.uint8)
+        uploaded_img = cv2.imdecode(uploaded_array, -1)
 
         # Save the uploaded file
-        filename = secure_filename(uploaded_file.name)
-        file_path = os.path.join(UPLOAD_FOLDER, filename)
-
-        with open(file_path, "wb") as f:
-            f.write(file_contents)
-
-        # Make prediction
-        result = predict_braintumor(file_path)
+        if isinstance(file_contents, bytes):
+            filename = secure_filename(uploaded_file.name)
+            file_path = os.path.join(UPLOAD_FOLDER, filename)
+
+            with open(file_path, "wb") as f:
+                f.write(file_contents)
+
+            # Make prediction
+            result = predict_braintumor(file_path)
+        else:
+            # Make prediction for Gradio (direct input of image)
+            result = predict_braintumor(uploaded_img)
 
         # Display prediction
         st.subheader("Prediction:")