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dic.py

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+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)

requirements.txt

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+opencv-python-headless
+streamlit
+numpy
+Pillow
+tensorflow

tb_classification_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3e31385eb92710b2e75ce67989885b32fbcadf88b4667dcabdcd613c5e927be3
+size 828735528