Update app.py

app.py

@@ -12,17 +12,6 @@ braintumor_model = load_model('models/brain_tumor_binary.h5')
 # Configuring Streamlit
 st.set_page_config(page_title="Brain Tumor Prediction App", page_icon=":brain:")
 
-# 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"]]
-)
-iface.launch()
-
 def preprocess_image(img):
     # If it's a NumPy array, use it directly
     if isinstance(img, np.ndarray):
@@ -56,3 +45,39 @@ 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 preprocess_imgs(set_name, img_size):
+    set_new = []
+    for img in set_name:
+        img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)
+        set_new.append(preprocess_input(img))
+    return np.array(set_new)
+
+def crop_imgs(set_name, add_pixels_value=0):
+    set_new = []
+    for img in set_name:
+        gray = cv2.GaussianBlur(img, (5, 5), 0)
+        thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
+        thresh = cv2.erode(thresh, None, iterations=2)
+        thresh = cv2.dilate(thresh, None, iterations=2)
+        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        cnts = cnts[0] if len(cnts) == 2 else cnts[1]
+        c = max(cnts, key=cv2.contourArea)
+        extLeft = tuple(c[c[:, :, 0].argmin()][0])
+        extRight = tuple(c[c[:, :, 0].argmax()][0])
+        extTop = tuple(c[c[:, :, 1].argmin()][0])
+        extBot = tuple(c[c[:, :, 1].argmax()][0])
+        ADD_PIXELS = add_pixels_value
+        new_img = img[extTop[1] - ADD_PIXELS:extBot[1] + ADD_PIXELS,
+                      extLeft[0] - ADD_PIXELS:extRight[0] + ADD_PIXELS].copy()
+        set_new.append(new_img)
+    return np.array(set_new)
+
+# 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()