Update app.py

app.py

@@ -12,6 +12,27 @@ braintumor_model = load_model('models/brain_tumor_binary.h5')
 # Configuring Streamlit
 st.set_page_config(page_title="Brain Tumor Prediction App", page_icon=":brain:")
 
+# Customizing Gradio appearance
+gr.set_config(
+    display_name=title,
+    interface_color="rgba(255, 99, 71, 0.8)",  # Adjust color as needed
+    live=True
+)
+
+# Configuring Gradio
+iface = gr.Interface(
+    fn="predict_braintumor",
+    inputs="image",
+    outputs="text",
+    live=True,
+    interpretation="default",
+    examples=[["examples/1_no.jpeg"], ["examples/2_no.jpeg"], ["examples/3_no.jpg"], ["examples/Y1.jpg"], ["examples/Y2.jpg"], ["examples/Y3.jpg"]],
+    title=title,
+    description=description,
+    article=article
+)
+iface.launch()
+
 def preprocess_image(img):
     # If it's a NumPy array, use it directly
     if isinstance(img, np.ndarray):
@@ -27,9 +48,7 @@ def preprocess_image(img):
         img_gray = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)
 
     # Crop and preprocess the grayscale image
-    img_processed = cv2.resize(img_gray, (224, 224), interpolation=cv2.INTER_CUBIC)
-    img_processed = preprocess_input(img_processed)
-    img_processed = np.expand_dims(img_processed, axis=0)
+    img_processed = preprocess_imgs([img_gray], (224, 224))
 
     return img_processed
 
@@ -47,33 +66,3 @@ def predict_braintumor(img):
     # Handle binary decision
     confidence = pred[0][0]
     return "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!"
-
-def main():
-    st.title("Brain Tumor Prediction App")
-
-    uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
-
-    if uploaded_file is not None:
-        st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True)
-        st.write("")
-        st.write("Classifying...")
-
-        # Make prediction
-        result = predict_braintumor(uploaded_file)
-
-        # Display prediction
-        st.subheader("Prediction:")
-        st.success(result)
-
-if __name__ == "__main__":
-    # Streamlit app
-    main()
-
-    # Gradio interface
-    iface = gr.Interface(
-        fn=predict_braintumor,
-        inputs="image",
-        outputs="text",
-        examples=[["examples/1_no.jpeg"], ["examples/2_no.jpeg"], ["examples/3_no.jpg"], ["examples/Y1.jpg"], ["examples/Y2.jpg"], ["examples/Y3.jpg"]]
-    )
-    iface.launch()