add basic app for pace prediction

app.py

@@ -0,0 +1,83 @@
+from pathlib import Path
+
+import numpy as np
+import tensorflow as tf
+import gradio as gr
+
+import cv2
+import keras
+from keras import Sequential
+from keras.layers import Flatten, Dense
+
+model_weights_path = (Path.cwd() / "pace_model_weights.h5").resolve()
+height, width, channels = (224, 224, 3)
+
+class PaceModel:
+    def __init__(self, height, width, channels):
+        self.resnet_model = Sequential()
+        self.height = height
+        self.width = width
+        self.channels = channels
+        self.class_names = ["Fast", "Medium", "Slow"]
+
+        self.create_pretrained()
+        self.create_architecture()
+    
+    def create_pretrained(self):
+        self.pretrained_model = tf.keras.applications.ResNet50(
+            include_top=False,
+            input_shape=(self.height, self.width, self.channels),
+            pooling="avg",
+            classes=211,
+            weights="imagenet"
+        )
+
+        for layer in self.pretrained_model.layers:
+            layer.trainable = False
+    
+    def create_architecture(self):
+        self.resnet_model.add(self.pretrained_model)
+        self.resnet_model.add(Flatten())
+        self.resnet_model.add(Dense(1024, activation="relu"))
+        self.resnet_model.add(Dense(256, activation="relu"))
+        self.resnet_model.add(Dense(3, activation="softmax"))
+
+        self.resnet_model.load_weights(model_weights_path)
+            
+    def predict(self, input_image: np.ndarray):
+        resized_image = cv2.resize(input_image, (self.height, self.width))
+        image = np.expand_dims(resized_image, axis=0)
+
+        prediction = self.resnet_model.predict(image)
+        print(prediction, np.argmax(prediction))
+        return self.class_names[np.argmax(prediction)]
+
+def main():
+    model = PaceModel(height, width, channels)
+
+    demo = gr.Interface(
+        fn=model.predict,
+        inputs=gr.Image(
+            type="numpy",
+            label="Upload an image",
+            show_label=True,
+            container=True
+        ),
+        outputs=gr.Textbox(
+            lines=1,
+            placeholder="Fast | Medium | Slow",
+            label="Pace of the image",
+            show_label=True,
+            container=True,
+            type="text"
+        ),
+        cache_examples=False,
+        live=False,
+        title="Predict Pace",
+        description="Provide an image to determine the pace of the image",
+    )
+
+    demo.queue().launch()
+
+if __name__ == "__main__":
+    main()