Update README.md

README.md

@@ -44,12 +44,44 @@ Validation Accuracy: ~96%
 Example (simplified):
 
 ```python
-model = Sequential([
-    Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),
-    MaxPooling2D(2,2),
-    Conv2D(64, (3,3), activation='relu'),
-    MaxPooling2D(2,2),
-    Flatten(),
-    Dense(128, activation='relu'),
-    Dense(1, activation='sigmoid')
-])
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from PIL import Image
+
+# Load the trained model
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model('models/brain_tumor_model.h5')  # Update path if needed
+
+model = load_model()
+
+# Define class labels
+class_names = ['glioma_tumor', 'meningioma_tumor', 'no_tumor', 'pituitary_tumor']
+
+# UI
+st.title("🧠 Brain Tumor Detection from MRI")
+st.write("Upload an MRI image to detect the type of brain tumor.")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose an MRI image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Show image
+    img = Image.open(uploaded_file)
+    st.image(img, caption="🖼️ Uploaded Image", use_container_width=True)
+
+    # Preprocessing
+    img = img.resize((224, 224))  # ✅ Make sure it matches your model's input size
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0) / 255.0
+
+    # Prediction
+    predictions = model.predict(img_array)
+    confidence = float(np.max(predictions)) * 100
+    predicted_class = class_names[np.argmax(predictions)]
+
+    # Output
+    st.success(f"🎯 Predicted Tumor Type: **{predicted_class}**")
+    st.info(f"📊 Model Confidence: **{confidence:.2f}%**")