--- a
+++ b/app.py
@@ -0,0 +1,93 @@
+import tensorflow as tf
+import streamlit as st
+import numpy as np
+from PIL import Image
+import io
+
+def load_model_with_custom_objects(model_path):
+    def custom_depthwise_conv2d(*args, **kwargs):
+        # Remove 'groups' argument if present
+        if 'groups' in kwargs:
+            del kwargs['groups']
+        return tf.keras.layers.DepthwiseConv2D(*args, **kwargs)
+    
+    custom_objects = {
+        'DepthwiseConv2D': custom_depthwise_conv2d,
+        'tf': tf
+    }
+    
+    try:
+        # Attempt to load with custom objects
+        model = tf.keras.models.load_model(
+            model_path, 
+            custom_objects=custom_objects,
+            compile=False
+        )
+        return model
+    except Exception as e:
+        st.error(f"Advanced loading failed: {e}")
+        return None
+
+def preprocess_image(image):
+    # Resize and normalize image
+    image = image.convert("RGB")
+    image = image.resize((224, 224))
+    image_array = np.array(image) / 255.0
+    image_array = np.expand_dims(image_array, axis=0)
+    return image_array
+
+def main():
+    st.title("Brain Tumor Detection Model Diagnostic")
+    
+    # Model path
+    model_path = 'models/keras_model.h5'
+    
+    # Load model
+    model = load_model_with_custom_objects(model_path)
+    
+    if model is None:
+        st.error("Could not load the model. Diagnosis needed.")
+        return
+    
+    # # Model summary
+    # st.write("Model Summary:")
+    # model.summary(print_fn=st.write)
+    
+    # # Model architecture details
+    # st.write("\nModel Configuration:")
+    # try:
+    #     config = model.get_config()
+    #     st.write(config)
+    # except Exception as e:
+    #     st.error(f"Could not retrieve model configuration: {e}")
+    
+    # Image upload for testing
+    uploaded_file = st.file_uploader("Upload a test image", type=['jpg', 'png', 'jpeg'])
+    
+    if uploaded_file is not None:
+        # Open and display the image
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        
+        # Preprocess and predict
+        try:
+            processed_image = preprocess_image(image)
+            predictions = model.predict(processed_image)
+            
+            # Assuming 4 classes as mentioned in previous code
+            CLASS_LABELS = ["Pituitary", "No Tumor", "Meningioma", "Glioma"]
+            
+            # Display prediction probabilities
+            st.write("Prediction Probabilities:")
+            for label, prob in zip(CLASS_LABELS, predictions[0]):
+                st.write(f"{label}: {prob*100:.2f}%")
+            
+            # Predicted class
+            predicted_class_index = np.argmax(predictions)
+            st.write(f"\nPredicted Class: {CLASS_LABELS[predicted_class_index]}")
+        
+        except Exception as pred_error:
+            st.error(f"Prediction failed: {pred_error}")
+
+if __name__ == "__main__":
+    main()