Update app.py

app.py

@@ -47,15 +47,20 @@ def crop_imgs(set_name, add_pixels_value=0):
     return np.array(set_new)
 
 # Function to preprocess the image
-def preprocess_image(file_path):
-    img = image.load_img(file_path, target_size=(200, 200))
+def preprocess_image(img_path):
+    img = image.load_img(img_path, 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
     return img_array
 
-def predict_braintumor(img_path):
-    img_gray = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
+def predict_braintumor(img_input):
+    if isinstance(img_input, str):  # File path (for Streamlit)
+        img_path = img_input
+        img_gray = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
+    else:  # NumPy array (for Gradio)
+        img_array = img_input.astype(np.uint8)
+        img_gray = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)
 
     # Crop and preprocess the grayscale image
     img_processed = crop_imgs([img_gray])
@@ -100,5 +105,10 @@ def main():
         st.success(result)
 
 if __name__ == "__main__":
-    main()
-    gr.Interface(fn=predict_braintumor, inputs="image", outputs="text").launch()
+    # Streamlit part
+    if "streamlit" in st.__version__.lower():
+        main()
+
+    # Gradio part
+    iface = gr.Interface(fn=predict_braintumor, inputs="image", outputs="text")
+    iface.launch()