Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import gradio.inputs
+import numpy as np # linear algebra
+import os #interacting with input and output directories
+import tensorflow as tf #framework for creating the neural network
+
+def fn(img):
+    img = np.expand_dims(img, axis = 0)
+    model = tf.keras.models.load_model(os.path.join(os.getcwd(), 'noteClassifierModel.h5'))
+    pred = model.predict(img)
+    pred = np.argmax(pred)
+
+    num_to_note_dict = {0: 'india_10', 1: 'india_100', 2: 'india_20',
+                        3: 'india_200', 4: 'india_2000', 5: 'india_50',
+                        6: 'india_500', 7: 'thai_100', 8: 'thai_1000',
+                        9: 'thai_20', 10: 'thai_50', 11:'thai_500'}
+
+    text_pred = num_to_note_dict[pred]
+
+    return text_pred
+    
+description = "This interface can be used to classify Indian and Thai currency notes into their correct " \
+              "denominations. For eg. if you upload an image of a Rs. 10 note, the output should be 'india_10'" \
+              ", similarly a 500 Baht note will output 'thai_500'. So what are you waiting for, go ahead and " \
+              "test the NoteClassifier..."
+iface = gr.Interface(fn,
+                     inputs= gradio.inputs.Image(tool="select", label = "Note Image", shape=(224, 224)),
+                     outputs='text',
+                     title="Note Classifier",
+                     description=description,
+                     theme="dark-seafoam",
+                     allow_flagging="manual")
+iface.launch(share=True)
+