Duplicate from viola77data/recycling-demo

Co-authored-by: viola meli <[email protected]>

.gitattributes

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README.md

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+---
+title: Recycling Demo
+emoji: ⚡
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 3.3.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: viola77data/recycling-demo
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import tensorflow as tf
+
+from keras.losses import SparseCategoricalCrossentropy
+from keras.metrics import SparseCategoricalAccuracy
+
+from PIL import Image
+import numpy as np
+
+from huggingface_hub import from_pretrained_keras
+
+import gradio as gr
+
+
+# prepare model
+model = from_pretrained_keras("viola77data/recycling")
+optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
+cls_loss = SparseCategoricalCrossentropy()
+cls_acc = SparseCategoricalAccuracy()
+model.compile(optimizer=optimizer, loss=cls_loss, metrics=[cls_acc])
+
+
+# prepare the categories
+categories = ['aluminium', 'batteries', 'cardboad', 
+            'disposable plates', 'glass', 'hard plastic',
+            'paper', 'paper towel', 'polystyrene',
+            'soft plastics', 'takeaway cups']
+
+dict_recycle = {
+    'aluminium': 'recycle',
+    'batteries': 'recycle',
+    'cardboad': 'recycle',
+    'disposable plates': 'dont recycle',
+    'glass': 'recycle', 
+    'hard plastic': 'recycle',
+    'paper': 'recycle', 
+    'paper towel': 'recycle', 
+    'polystyrene': ' dont recycle',
+    'soft plastics': 'dont recycle', 
+    'takeaway cups': 'dont recycle'
+}
+
+
+# prediction functions
+def preprocess_image(im):
+    """ Pass in a numpy image an it returns a
+    TF Image"""
+    im = tf.cast(im, tf.float32) / 255.0
+    if len(im.shape) < 3:
+        im = tf.expand_dims(im, axis=-1)  # add the channel dimension
+        im = tf.image.grayscale_to_rgb(im)
+    im = tf.image.resize(im, (224, 224))
+    im = tf.expand_dims(im, axis=0)
+
+    return im
+
+
+def classify_image(input):
+    input_processed = preprocess_image(input)
+    preds = model.predict(input_processed)[0]
+
+    cls_preds = dict(zip(categories, map(float, preds)))
+
+    predicted_class = categories[np.argmax(preds)]
+    recycle_preds = dict_recycle[predicted_class]
+
+    return cls_preds, recycle_preds
+
+
+
+# Defining the Gradio Interface
+# This is how the Demo will look like.
+title = "Should I Recycle This?"
+description = """
+
+This app was created to help people recycle the right type of waste.
+
+You can use it at the comfort of your own home. Just take a picture of the waste material you want to know if
+its recyclible and upload it to this app and using Artificial Intelligence it will determine if you should
+throw the waste in the recycling bin or the normal bin.
+
+Enjoy!
+
+Made by Viola, you can reach out to me here: <a href="[email protected]">Send Email</a> 
+
+
+"""
+
+image = gr.Image(shape=(224,224))
+label = gr.Label(num_top_classes=3, label='Prediction Material')
+recycle = gr.Textbox(label='Should you recycle?')
+outputs = [label, recycle]
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=outputs, title = title, description = description, 
+                    cache_examples=False)
+intf.launch(enable_queue=True)

requirements.txt

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+tensorflow==2.9.1
+keras==2.9.0