dic.py

import streamlit as st
import numpy as np
import cv2
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import matplotlib.pyplot as plt
from PIL import Image
import gdown

# Load trained model
# model_path = "tb_classification_model.h5"  # Ensure this file exists
# model = tf.keras.models.load_model(model_path)

file_id = "1S6Keu4Qmaj6NrtX3lF3s9tbUFlyYhDhL"
output_path = "tb_classification_model.h5"

# Download the model
gdown.download(f"https://drive.google.com/uc?id={file_id}", output_path, quiet=False)

# Load the model
model = tf.keras.models.load_model(output_path)


def preprocess_image(img):
    img = img.resize((224, 224))  # Resize to model input size
    img_array = image.img_to_array(img)  

    # Convert grayscale images to 3-channel RGB
    if img_array.shape[-1] == 1:
        img_array = np.repeat(img_array, 3, axis=-1)  # (224, 224, 1) → (224, 224, 3)

    img_array = img_array / 255.0  # Normalize
    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension

    return img_array  # ✅ Return processed image


# Function to compute Grad-CAM
def compute_gradcam(model, img_array, layer_name="conv4_block5_out"):
    grad_model = tf.keras.models.Model(inputs=model.input, 
                                       outputs=[model.get_layer(layer_name).output, model.output])

    with tf.GradientTape() as tape:
        conv_outputs, predictions = grad_model(img_array)
        class_idx = tf.argmax(predictions[0])
        loss = predictions[:, class_idx]

    grads = tape.gradient(loss, conv_outputs)
    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))

    conv_outputs = conv_outputs.numpy()[0]
    heatmap = np.dot(conv_outputs, pooled_grads.numpy())

    heatmap = np.maximum(heatmap, 0)  # ReLU
    heatmap /= np.max(heatmap) + 1e-10  # Normalize
    return heatmap

# Function to overlay Grad-CAM heatmap
def overlay_gradcam(img, heatmap, alpha=0.4):
    """Overlay Grad-CAM heatmap on the original image."""
    
    # Ensure heatmap is the same size as the image
    heatmap = cv2.resize(heatmap, (img.width, img.height))  # Resize to match original image
    
    # Normalize heatmap to [0,255] and apply color mapping
    heatmap = np.uint8(255 * heatmap)
    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)

    # Convert PIL image to NumPy array
    original = np.array(img)

    # If original image is grayscale, convert to RGB
    if len(original.shape) == 2 or original.shape[-1] == 1:
        original = cv2.cvtColor(original, cv2.COLOR_GRAY2RGB)

    # Ensure both images have the same shape (H, W, 3)
    if heatmap.shape != original.shape:
        heatmap = cv2.resize(heatmap, (original.shape[1], original.shape[0]))

    # Blend original image with heatmap
    superimposed = cv2.addWeighted(original, 1 - alpha, heatmap, alpha, 0)

    return superimposed

# Streamlit UI
st.title("Tuberculosis Detection using ResNet50")
st.write("Upload a Chest X-ray image to classify as **Normal** or **TB**.")

uploaded_file = st.file_uploader("Choose a Chest X-ray Image...", type=["jpg", "png", "jpeg"])

if uploaded_file is not None:
    image_pil = Image.open(uploaded_file)
    st.image(image_pil, caption="Uploaded Image", use_container_width=True)

    # Preprocess image
    img_array = preprocess_image(image_pil)

    # Get model prediction
    prediction = model.predict(img_array)[0][0]
    result = "Tuberculosis Detected" if prediction > 0.5 else "Normal"
    confidence = prediction if prediction > 0.5 else 1 - prediction

    st.subheader(f"Prediction: **{result}**")
    st.write(f"Confidence: **{confidence:.2%}**")

    # Generate Grad-CAM
    heatmap = compute_gradcam(model, img_array)
    gradcam_output = overlay_gradcam(image_pil, heatmap)

    # Show Grad-CAM
    st.subheader("Grad-CAM Visualization")
    st.image(gradcam_output, caption="Grad-CAM Heatmap", use_container_width=True)