Update app.py

app.py

@@ -8,7 +8,7 @@ import json
 with open("class_indices.json", "r") as f:
     class_labels = json.load(f)
 
-# Load trained model
+# Load model
 model = tf.keras.models.load_model("best_model.h5")
 
 def preprocess(img: Image.Image) -> np.ndarray:
@@ -18,23 +18,45 @@ def preprocess(img: Image.Image) -> np.ndarray:
 
 def predict_disease(image: Image.Image) -> str:
     try:
-        processed_img = preprocess(image)
-        preds = model.predict(processed_img)
+        img = preprocess(image)
+        preds = model.predict(img)
         idx = np.argmax(preds)
         label = class_labels[str(idx)]
         confidence = preds[0][idx]
-        return f"**Prediction:** {label}\n**Confidence:** {confidence:.2%}"
+        return f"### Prediction Result\n**Disease:** {label}\n**Confidence:** {confidence:.2%}"
     except Exception as e:
         return f"Prediction error: {e}"
 
+css = """
+.gradio-container {
+    max-width: 600px;
+    margin: auto;
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+}
+h1, h3 {
+    color: #2c3e50;
+}
+.gr-button {
+    background-color: #27ae60;
+    border-radius: 5px;
+    border: none;
+    color: white;
+    font-weight: bold;
+}
+.gr-button:hover {
+    background-color: #219150;
+}
+"""
+
 interface = gr.Interface(
     fn=predict_disease,
-    inputs=gr.Image(type="pil", label="Upload Tomato Leaf Image"),
-    outputs=gr.Markdown(label="Result"),
-    title="Tomato Leaf Disease Detection",
-    description="Upload a tomato leaf image to identify the disease with confidence.",
-    theme="default",
+    inputs=gr.Image(type="pil", label="Upload a clear image of a tomato leaf"),
+    outputs=gr.Markdown(label="Detection Result"),
+    title="🍅 Tomato Leaf Disease Detector",
+    description="Upload a tomato leaf image to detect diseases with confidence using a trained TensorFlow model.",
     allow_flagging="never",
+    theme="default",
+    css=css,
 )
 
 if __name__ == "__main__":
@@ -42,8 +64,6 @@ if __name__ == "__main__":
 
 
 
-
-
 # ----------------------------------------------------------------------------------------------------------------------
 
 # import gradio as gr