Update app.py

app.py

@@ -5,7 +5,6 @@ import numpy as np
 from tensorflow.keras.models import load_model
 from tensorflow.keras.applications.vgg16 import preprocess_input
 from tensorflow.keras.preprocessing import image
-from werkzeug.utils import secure_filename
 import os
 
 # Loading Models
@@ -16,9 +15,6 @@ UPLOAD_FOLDER = 'static/uploads'
 ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
 st.set_page_config(page_title="Brain Tumor Prediction App", page_icon=":brain:")
 
-def allowed_file(filename):
-    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
-
 def preprocess_imgs(set_name, img_size):
     set_new = []
     for img in set_name:
@@ -58,13 +54,16 @@ def preprocess_image(file_path):
 def binary_decision(confidence):
     return 1 if confidence >= 0.5 else 0
 
-def predict_braintumor(file_contents):
+def predict_braintumor(img):
     # Save the uploaded file
     filename = "temp_image.png"
     file_path = os.path.join(UPLOAD_FOLDER, filename)
 
+    # Convert Gradio image data to bytes
+    img_bytes = img.read()
+    
     with open(file_path, "wb") as f:
-        f.write(file_contents)
+        f.write(img_bytes)
 
     img_gray = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
 
@@ -77,7 +76,7 @@ def predict_braintumor(file_contents):
 
     # Handle binary decision
     confidence = pred[0][0]
-    return binary_decision(confidence)
+    return "Brain Tumor Found!" if binary_decision(confidence) == 1 else "Brain Tumor Not Found!"
 
 def main():
     st.title("Brain Tumor Prediction App")
@@ -89,18 +88,12 @@ def main():
         st.write("")
         st.write("Classifying...")
 
-        # Read the contents of the uploaded file
-        file_contents = uploaded_file.read()
-
         # Make prediction
-        result = predict_braintumor(file_contents)
+        result = predict_braintumor(uploaded_file)
 
         # Display prediction
         st.subheader("Prediction:")
-        if result == 1:
-            st.success("Brain Tumor Found!")
-        else:
-            st.success("Brain Tumor Not Found!")
+        st.success(result)
 
 if __name__ == "__main__":
     # Streamlit app