app.py

import streamlit as st
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('brain_tumor_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_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.write("Upload a brain scan (JPG format), and the model will predict its class.")

# File uploader for user to upload images
uploaded_file = st.file_uploader("Choose a JPG image...", type="jpg")

if uploaded_file is not None:
    # Display the uploaded image on the left side
    col1, col2 = st.columns([2, 1])  # Create two columns

    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)