Update app.py

app.py

@@ -1,30 +1,33 @@
 import streamlit as st
 import numpy as np
-from keras.models import load_model
-from keras.preprocessing import image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
 from PIL import Image
 
-# Load model (make sure brain_tumor_model.h5 is in same folder)
-model = load_model("brain_tumor_model.h5")
+# Load the updated Keras model
+model = load_model("clean_model.keras")
 
-# Set title
-st.title("🧠 Brain Tumor Detection using CNN")
+st.title("Disease Prediction App")
+st.write("Upload an image and get prediction")
 
-# File uploader
-uploaded_file = st.file_uploader("Upload an MRI scan image", type=["jpg", "jpeg", "png"])
+# Upload image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 if uploaded_file is not None:
-    # Show image
+    # Display the uploaded image
     img = Image.open(uploaded_file)
     st.image(img, caption="Uploaded Image", use_column_width=True)
 
-    # Preprocessing
-    img = img.resize((64, 64))  # adjust size to match model input
+    # Preprocess image
+    img = img.resize((64, 64))  # Replace with your model's expected input size
     img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0) / 255.0  # Normalize
+    img_array = np.expand_dims(img_array, axis=0) / 255.0
 
     # Predict
     prediction = model.predict(img_array)
-    result = "🧠 Brain Tumor Detected" if prediction[0][0] > 0.5 else "✅ No Brain Tumor Detected"
 
-    st.markdown(f"### Result: {result}")
+    # Result
+    if prediction[0][0] > 0.5:
+        st.write("🔴 Disease Detected")
+    else:
+        st.write("🟢 No Disease Detected")