Update app.py

app.py

@@ -137,68 +137,81 @@
 
 
 
+import gradio as gr
 import numpy as np
-from tensorflow.keras.models import load_model
 from PIL import Image
-import gradio as gr
-from models import create_vgg19_model
+import cv2
+import tensorflow as tf
+from tensorflow.keras.models import load_model
 from gradcam_utils import generate_heatmap_tf_explain
+from models import create_vgg19_model
 
-# Load models
+# Load your trained model
 ensemble_model = load_model("ensemble_model_best(92.3).h5")
-vgg_model = create_vgg19_model()  # Used only for Grad-CAM
+vgg_model = create_vgg19_model()
 
 def get_class_name(class_id):
     return "Normal" if class_id == 0 else "Pneumonia"
 
 def predict_and_heatmap(image):
-    # Resize and normalize image for prediction
     img = image.resize((224, 224))
     img_array = np.array(img) / 255.0
     img_array = np.expand_dims(img_array, axis=0)
 
-    # Predict with ensemble model
     prediction = ensemble_model.predict(img_array)
     class_id = int(np.argmax(prediction[0]))
-    result = get_class_name(class_id)
-
-    # Generate heatmap with tf-explain using VGG19
-    heatmap_img = generate_heatmap_tf_explain(img, vgg_model, class_index=class_id)
-
-    return result, heatmap_img
-
-# 🎨 Custom CSS styling
-custom_css = """
-body {
-    background-color: #1c1c1e;
-    font-family: 'Segoe UI', sans-serif;
-}
-h1, h2, .output_class {
-    color: #ffffff;
-    text-align: center;
-}
-.gr-button {
-    background-color: #007aff !important;
-    color: white !important;
-}
-.gr-image-preview {
-    box-shadow: 0 0 20px rgba(0,0,0,0.5);
-    border-radius: 8px;
-}
-"""
-
-# Launch Gradio Interface
-interface = gr.Interface(
-    fn=predict_and_heatmap,
-    inputs=gr.Image(type="pil", label="Upload Chest X-ray"),
-    outputs=[
-        gr.Label(label="Prediction"),
-        gr.Image(label="Grad-CAM Heatmap")
-    ],
-    title="🩺 Pneumonia Detection Using Deep Learning",
-    description="Upload a chest X-ray to detect Pneumonia and see the heatmap visualization (powered by tf-explain and VGG19).",
-    css=custom_css
-)
+    label = get_class_name(class_id)
+
+    # Styled HTML result
+    result_html = f"""
+    <div style='
+        text-align: center;
+        font-size: 1.5rem;
+        font-weight: bold;
+        color: {"green" if class_id == 0 else "red"};
+        background-color: #f0f8ff;
+        border: 2px solid {"green" if class_id == 0 else "red"};
+        padding: 10px;
+        border-radius: 10px;
+        width: fit-content;
+        margin: 0 auto;
+    '>
+        Result: {label}
+    </div>
+    """
+
+    # Generate heatmap from VGG19 using tf-explain
+    heatmap_img = generate_heatmap_tf_explain(image, vgg_model, class_index=class_id)
+    return result_html, heatmap_img
+
+# Styled Gradio Interface
+with gr.Blocks(theme="soft") as demo:
+    gr.Markdown("""
+    <div style="text-align: center; font-size: 2.5rem; font-weight: bold; color: #0b5394; margin-bottom: 1rem;">
+        🩺 Pneumonia Detection from Chest X-rays
+    </div>
+    <div style="text-align: center; font-size: 1.1rem; margin-bottom: 2rem;">
+        Upload a chest X-ray image to predict if the lungs are Normal or show signs of Pneumonia.
+    </div>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1, min_width=600):
+            image_input = gr.Image(type="pil", label="Upload Chest X-Ray", interactive=True, width=600, height=600)
+            prediction_output = gr.HTML(label="Prediction")
+            heatmap_output = gr.Image(label="Grad-CAM Heatmap")
+            submit_button = gr.Button("Predict")
+            clear_button = gr.Button("Clear")
+
+    submit_button.click(fn=predict_and_heatmap, inputs=image_input, outputs=[prediction_output, heatmap_output])
+    clear_button.click(fn=lambda: (None, "", None), inputs=[], outputs=[image_input, prediction_output, heatmap_output])
+
+    gr.Markdown("""
+    <div style="text-align: center; font-size: 0.95rem; color: #888; margin-top: 30px;">
+        Made with ❤️ by <a href="https://github.com/hruthik733" target="_blank" style="color: #0b5394; text-decoration: none; font-weight: bold;">
+        Hruthik Pavarala</a>
+    </div>
+    """)
 
 if __name__ == "__main__":
-    interface.launch()
+    demo.launch()