commit

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import PIL
+import tensorflow as tf
+
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Convolution2D
+from tensorflow.keras.layers import MaxPooling2D
+from tensorflow.keras.layers import Flatten
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.layers import Dropout
+
+from tensorflow.keras.callbacks import EarlyStopping
+
+
+from tensorflow.keras.models import load_model
+ 
+# load model
+model = load_model('model11.h5')
+
+classnames = ['cardboard','metal','paper','plastic','trash','green-glass','white-glass','brown-glass','clothes','biological','battery','shoes']
+
+def predict_image(img):
+  img_4d=img.reshape(-1,298, 384,3)
+  prediction=model.predict(img_4d)[0]
+  return {classnames[i]: float(prediction[i]) for i in range(12)}
+
+sample_images = [
+                 ["battery.JPG"],
+                 ["jeans.jpg"],
+                 ["paper1.jpg"]]
+                
+
+image = gr.inputs.Image(shape=(298, 384))
+label = gr.outputs.Label(num_top_classes=3)
+
+gr.Interface(fn=predict_image, inputs=image,  title="Garbage Classifier",
+    description="This is the Model uploaded on Github trained on Dataset 11.This is just for a demo to test the deployment of Tensorflow Based Models into Gradio + Hugging Faces.",outputs=label,examples=sample_images,interpretation='default').launch(debug='True')

model11.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:09b7f5de5f07156815ddcd45e8ec0ea17ff998eaf6d4a35cb181c29fde6b6f67
+size 153628864

requirements.txt

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+tensorflow
+numpy
+requests
+Pillow
+matplotlib