Update app.py

app.py

@@ -7,6 +7,34 @@ def predict(img):
     img = PILImage.create(img)
     pred,pred_idx,probs = learn.predict(img)
     return {labels[i]: float(probs[i]) for i in range(len(labels))}
-title='Emergency Vehicle Classifier'
+
+# Read the image file and encode it as base64
+with open("./1001epochs.png", "rb") as f:
+    image_data = f.read()
+    image_base64 = base64.b64encode(image_data).decode("utf-8")
+
+allow_flagging = "never"
+
+title = f"""
+    <h2 style="background-image: linear-gradient(to right, #3A5FCD, #87CEFA); -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent; text-align: center;">
+        Emergency Vehicle Classifier
+    </h2>
+"""
+
+description = f"""
+<div style="display: flex; align-items: center; justify-content: center; flex-direction: column;">
+    <p style="font-size: 18px; color: #4AAAFF; text-align: center;">
+        Simply upload a photo and let our sophisticated AI system determine the specific type of emergency vehicle depicted.
+    </p>
+    <div style="display: flex; align-items: center; margin-bottom: 0px;">
+        <img src='data:image/jpeg;base64,{image_base64}' width='50' height='30' style="margin-right: 5px;"/>
+        <p style="font-size: 14px; color: #555;">
+            Disclaimer: This web app is for demonstration purposes only and not intended for commercial use. Contact: [email protected] for full solution.
+        </p>
+    </div>
+</div>
+"""
+
 interpretation='default'
-gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3), title=title, interpretation=interpretation).launch(share=True)
+gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(512, 512)), outputs=gr.outputs.Label(num_top_classes=3), title=title, interpretation=interpretation, description=description).launch()