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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import tensorflow.keras as keras
+
+from tensorflow.keras.models import load_model
+ 
+# load model
+model = load_model('model18.h5')
+
+classnames = ['battery','biological','brown-glass','cardboard','clothes','green-glass','metal','paper','plastic','shoes','trash','white-glass']
+
+
+
+def predict_image(img):
+  img_4d=img.reshape(-1,224, 224,3)
+  prediction=model.predict(img_4d)[0]
+  return {classnames[i]: float(prediction[i]) for i in range(12)}
+
+                
+
+image = gr.inputs.Image(shape=(224, 224))
+label = gr.outputs.Label(num_top_classes=3)
+article="<p style='text-align: center'>Made by Aditya Narendra with 🖤</p>"
+
+gr.Interface(fn=predict_image, inputs=image,  title="Garbage Classifier",
+    description="This is a Garbage Classification Model Trained using MobileNetV2.Deployed to Hugging Faces using Gradio.",outputs=label,article=article,interpretation='default').launch(share="True")