Update app.py

app.py

@@ -1,24 +1,72 @@
 import gradio as gr
 from tensorflow.keras.models import load_model
-from PIL import Image
 import numpy as np
+from PIL import Image
 
+# Load the trained model
 model = load_model('xray_image_classifier_model.keras')
 
-def predict_image(img):
-    img = img.resize((150, 150))
-    img = np.array(img) / 255.0
-    img = np.expand_dims(img, axis=0)
-    prediction = model.predict(img)
-    label = 'Pneumonia' if prediction > 0.5 else 'Normal'
-    return label
-
-iface = gr.Interface(
-    fn=predict_image, 
-    inputs=gr.Image(type="pil"), 
-    outputs="text",
-    title="X-ray Image Classifier",
-    description="Upload an X-ray image to classify it as 'Pneumonia' or 'Normal'."
-)
-
-iface.launch()
+def predict(image):
+    # Preprocess the input image
+    img = image.resize((150, 150))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Make a prediction
+    prediction = model.predict(img_array)
+    predicted_class = 'Pneumonia' if prediction > 0.5 else 'Normal'
+
+    return predicted_class
+
+# Custom CSS for the interface
+css = """
+    .gradio-container {
+        background-color: #f5f5f5;
+        font-family: Arial, sans-serif;
+    }
+    .gr-button {
+        background-color: #007bff;
+        color: white;
+        border-radius: 5px;
+        font-size: 16px;
+    }
+    .gr-button:hover {
+        background-color: #0056b3;
+    }
+    .gr-textbox, .gr-image {
+        border: 2px dashed #007bff;
+        padding: 20px;
+        border-radius: 10px;
+        background-color: #ffffff;
+    }
+    .gr-box-text {
+        color: #007bff;
+        font-size: 22px;
+        font-weight: bold;
+        text-align: center;
+    }
+    h1 {
+        font-size: 36px;
+        color: #007bff;
+        text-align: center;
+    }
+    p {
+        font-size: 20px;
+        color: #333;
+        text-align: center;
+    }
+"""
+
+# Gradio interface
+with gr.Blocks(css=css) as interface:
+    gr.Markdown("<h1>Chest X-ray Pneumonia Classifier</h1>")
+    gr.Markdown("<p>Upload an X-ray image to classify it as 'Pneumonia' or 'Normal'.</p>")
+    
+    with gr.Row():
+        image_input = gr.Image(label="Drop Image Here", type="pil", elem_classes=["gr-image", "gr-box-text"])
+        output = gr.Textbox(label="Prediction", elem_classes=["gr-textbox", "gr-box-text"])
+
+    submit_btn = gr.Button("Classify X-ray", elem_classes=["gr-button"])
+    submit_btn.click(fn=predict, inputs=image_input, outputs=output)
+
+interface.launch()