initial commit

.gitignore

@@ -0,0 +1,7 @@
+flagged/
+*.pt
+*.png
+*.jpg
+*.mp4
+*.mkv
+gradio_cached_examples/

main.py

@@ -0,0 +1,77 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+# Load the TFLite model
+interpreter = tf.lite.Interpreter(model_path=r"D:\Projects\Computer Vision\cat_and_dog\saved_models\converted_models\quantization_DEFAULT_8bit_model_h5_to_tflite.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+CLASS_NAMES = ['Abyssinian',
+ 'Bengal',
+ 'Birman',
+ 'Bombay',
+ 'British_Shorthair',
+ 'Egyptian_Mau',
+ 'Maine_Coon',
+ 'Persian',
+ 'Ragdoll',
+ 'Russian_Blue',
+ 'Siamese',
+ 'Sphynx',
+ 'american_bulldog',
+ 'american_pit_bull_terrier',
+ 'basset_hound',
+ 'beagle',
+ 'boxer',
+ 'chihuahua',
+ 'english_cocker_spaniel',
+ 'english_setter',
+ 'german_shorthaired',
+ 'great_pyrenees',
+ 'havanese',
+ 'japanese_chin',
+ 'keeshond',
+ 'leonberger',
+ 'miniature_pinscher',
+ 'newfoundland',
+ 'pomeranian',
+ 'pug',
+ 'saint_bernard',
+ 'samoyed',
+ 'scottish_terrier',
+ 'shiba_inu',
+ 'staffordshire_bull_terrier',
+ 'wheaten_terrier',
+ 'yorkshire_terrier']
+
+IMAGE_SIZE = 256
+
+def predict_image(image: Image.Image):
+    image = image.convert("RGB")
+    image = image.resize((IMAGE_SIZE, IMAGE_SIZE))
+    image_np = np.array(image) / 255.0
+    img_batch = np.expand_dims(image_np, 0).astype(np.float32)
+
+    interpreter.set_tensor(input_details[0]['index'], img_batch)
+    interpreter.invoke()
+    output = interpreter.get_tensor(output_details[0]['index'])[0]
+    predicted_class = CLASS_NAMES[np.argmax(output)]
+    confidence = float(np.max(output))
+    return predicted_class, f"{confidence:.2f}"
+
+# Gradio interface
+interface = gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Label(label="Predicted Class"), gr.Label(label="Confidence")],
+    title="Cat & Dog Breed Classifier",
+    description="Upload an image of a cat or dog to identify its breed."
+)
+
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

@@ -0,0 +1,8 @@
+tensorflow==2.17.1
+fastapi
+uvicorn
+python-multipart
+pillow
+tensorflow-serving-api==2.17.1
+matplotlib
+numpy