Update app.py

app.py

@@ -1,52 +1,65 @@
 import streamlit as st
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
 import numpy as np
+import cv2
+import tensorflow as tf
 from PIL import Image
+from sklearn.preprocessing import LabelEncoder
+
+# Load your pre-trained model (Make sure this matches the version used during training)
+model = tf.keras.models.load_model('dementia_cnn_model.h5')
+
+# Example class labels (update this list with your actual class labels)
+class_labels = ['glioma', 'pituitary', 'meningioma', 'healthy']
+label_encoder = LabelEncoder()
+label_encoder.fit(class_labels)  # Fit the label encoder with your class labels
 
 # Function to load and preprocess the uploaded image
-def load_and_preprocess_image(uploaded_image, target_size=(224, 224)):
-    img = Image.open(uploaded_image)  # Open the uploaded image
-    img = img.resize(target_size)     # Resize to match model's input shape
-    img_array = np.array(img)         # Convert to numpy array
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-    img_array = img_array / 255.0     # Normalize pixel values
-    return img_array
-
-# Load your pre-trained model (assuming it's in HDF5 format)
-@st.cache(allow_output_mutation=True)  # Cache the model to avoid reloading it each time
-def load_cnn_model():
-    model = load_model('brain_tumor_model.h5')  # Replace with your model path
-    return model
+def load_and_preprocess_image(uploaded_file):
+    img = Image.open(uploaded_file)
+    img = img.convert("RGB")  # Convert to RGB if it's in another format
+    img = np.array(img)  # Convert to NumPy array
+    img = cv2.resize(img, (224, 224))  # Resize the image to 224x224
+    img = img / 255.0  # Normalize pixel values
+    img = np.reshape(img, (1, 224, 224, 3))  # Reshape for prediction
+    return img
+
+# Function to predict the image class
+def predict_image(img):
+    predictions = model.predict(img)  # Make a prediction
+    predicted_class_index = np.argmax(predictions[0])  # Get the predicted class index
+    return predicted_class_index
+
+# Function to get class label
+def get_class_label(predicted_class_index):
+    return label_encoder.inverse_transform([predicted_class_index])[0]  # Get class label
 
 # Streamlit App UI
-st.title("Brain Tumor using CNN")
+st.title("Brain Tumor using CNN 🧠")
 st.write("Upload a brain scan (JPG format), and the model will predict its class.")
 
-# Upload image button
+# File uploader for user to upload images
 uploaded_file = st.file_uploader("Choose a JPG image...", type="jpg")
 
-# If an image is uploaded, process it
 if uploaded_file is not None:
-    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
-    st.write("Classifying...")
-
-    # Preprocess the uploaded image
-    processed_image = load_and_preprocess_image(uploaded_file, target_size=(224, 224))
-
-    # Load the model
-    model = load_cnn_model()
-
-    # Make predictions
-    predictions = model.predict(processed_image)
-    predicted_class = np.argmax(predictions, axis=1)
-
-    # Map the class index to the actual class names
-    class_names = ['glioma', 'pituitary', 'meningioma', 'healthy']
-    result = class_names[predicted_class[0]]
+    # Display the uploaded image on the left side
+    col1, col2 = st.columns([2, 1])  # Create two columns
 
-    # Display the prediction
-    st.write(f"Predicted Class: **{result}**")
-else:
-    st.write("Please upload an image to classify.")
+    with col1:
+        st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+    
+    with col2:
+        # Button to trigger prediction
+        if st.button("Detect"):
+            st.write("Detecting...")
+            # Load and preprocess the image
+            processed_image = load_and_preprocess_image(uploaded_file)
+            
+            # Make prediction
+            predicted_class_index = predict_image(processed_image)
+            
+            # Get predicted class label
+            predicted_class_label = get_class_label(predicted_class_index)
+            
+            # Center display for the prediction result
+            st.markdown(f"<h3 style='color: #4CAF50; text-align: center;'>The Prediction is : <strong>{predicted_class_label}</strong></h3>", unsafe_allow_html=True)