Update app.py

app.py

@@ -47,21 +47,19 @@ def crop_imgs(set_name, add_pixels_value=0):
     return np.array(set_new)
 
 # Function to preprocess the image
-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))
+def preprocess_image(file_path):
+    img = image.load_img(file_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_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)
+# Handle binary decision
+def binary_decision(confidence):
+    return 1 if confidence >= 0.5 else 0
+
+def predict_braintumor(img_path):
+    img_gray = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
 
     # Crop and preprocess the grayscale image
     img_processed = crop_imgs([img_gray])
@@ -71,9 +69,8 @@ def predict_braintumor(img_input):
     pred = braintumor_model.predict(img_processed)
 
     # Handle binary decision
-    pred = 1 if pred[0] >= 0.5 else 0
-
-    return pred
+    confidence = pred[0][0]
+    return binary_decision(confidence)
 
 def main():
     st.title("Brain Tumor Prediction App")
@@ -85,48 +82,36 @@ def main():
         st.write("")
         st.write("Classifying...")
 
-        # Read the contents of the uploaded file or the input image
+        # Read the contents of the uploaded file
         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
-        if isinstance(file_contents, bytes):
-            filename = secure_filename(uploaded_file.name)
-            file_path = os.path.join(UPLOAD_FOLDER, filename)
+        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)
+        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)
+        # Make prediction
+        result = predict_braintumor(file_path)
 
-        # Handle binary decision
-        result_text = "Brain Tumor Found!" if result == 1 else "Brain Tumor Not Found!"
-        
         # Display prediction
         st.subheader("Prediction:")
-        st.success(result_text)
+        if result == 1:
+            st.success("Brain Tumor Found!")
+        else:
+            st.success("Brain Tumor Not Found!")
 
 if __name__ == "__main__":
-    # Streamlit part
-    if "streamlit" in st.__version__.lower():
-        main()
+    # Streamlit app
+    main()
 
-    # Gradio part
+    # Gradio interface
     iface = gr.Interface(
         fn=predict_braintumor,
         inputs="image",
         outputs="text",
-        examples = [
-    ["examples/1 no.jpeg"],
-    ["examples/2 no.jpeg"],
-    ["examples/3 yes.jpg"],
-    ["examples/4 yes.jpg"]
-],
+        examples=[["examples/1_no.jpg"], ["examples/2_no.jpg"], ["examples/3_yes.jpg"], ["examples/4_yes.jpg"]],
         enable_queue=True
     )
     iface.launch()