Update app.py

app.py

@@ -10,24 +10,36 @@ import PIL
 import io
 import matplotlib.pyplot as plt
 
-def download_image(url):
-    resp = requests.get(url)
-    resp.raise_for_status()
-    return PIL.Image.open(io.BytesIO(resp.content))
 
 from keras_cv_attention_models import convnext
 
-mm = convnext.ConvNeXtBase()
-
-downloaded_image = download_image(
-    "https://www.popsci.com/uploads/2021/09/21/Tortoise-on-ground-surrounded-by-plants.jpg?auto=webp"
-    )
+import gradio as gr
 
-downloaded_image_np = np.array(downloaded_image)
-
-img = downloaded_image_np
-imm = keras.applications.imagenet_utils.preprocess_input(img, mode='torch')
-image_input = tf.expand_dims(tf.image.resize(imm, mm.input_shape[1:3]), 0)
+mm = convnext.ConvNeXtBase()
 
-pred = mm(image_input)
-pred_np = pred.numpy()
+def(img):
+    img = img
+    imm = keras.applications.imagenet_utils.preprocess_input(img, mode='torch')
+    image_input = tf.expand_dims(tf.image.resize(imm, mm.input_shape[1:3]), 0)
+    
+    pred = mm(image_input)
+    pred_np = pred.numpy()
+    
+   pred_names = keras.applications.imagenet_utils.decode_predictions(pred.numpy())[0]
+    
+    result = {}
+    
+    for i in range(5):
+        result[pred_names[i][1]] = int(100*pred_names[i][2]).item()
+    
+    return result
+
+inputs = gr.inputs.Image(type='numpy')
+outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
+
+title = "MOBILENET V2"
+description = "Gradio demo for MOBILENET V2, Efficient networks optimized for speed and memory, with residual blocks. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1801.04381'>MobileNetV2: Inverted Residuals and Linear Bottlenecks</a> | <a href='https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenet.py'>Github Repo</a></p>"
+
+
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, analytics_enabled=False).launch()