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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+mySpiritualAnimalResNet50V2.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load your custom regression model
+model_path = "mySpiritualAnimalResNet50V2.keras"
+model = tf.keras.models.load_model(model_path)
+model.summary()  # Check if the model architecture loaded matches the expected one
+
+labels = ['antelope', 'badger', 'bat', 'bear', 'bee', 'beetle', 'bison', 'boar', 'butterfly', 'cat', 'caterpillar', 'chimpanzee', 'cockroach', 'cow', 'coyote', 'crab', 'crow', 'deer', 'dog', 'dolphin', 'donkey', 'dragonfly', 'duck', 'eagle', 'elephant', 'flamingo', 'fly', 'fox', 'goat', 'goldfish', 'goose', 'gorilla', 'grasshopper', 'hamster', 'hare', 'hedgehog', 'hippopotamus', 'hornbill', 'horse', 'hummingbird', 'hyena', 'jellyfish', 'kangaroo', 'koala', 'ladybugs', 'leopard', 'lion', 'lizard', 'lobster', 'mosquito', 'moth', 'mouse', 'octopus', 'okapi', 'orangutan', 'otter', 'owl', 'ox', 'oyster', 'panda', 'parrot', 'pelecaniformes', 'penguin', 'pig', 'pigeon', 'porcupine', 'possum', 'raccoon', 'rat', 'red_panda', 'reindeer', 'rhinoceros', 'sandpiper', 'seahorse', 'seal', 'shark', 'sheep', 'snake', 'sparrow', 'squid', 'squirrel', 'starfish', 'swan', 'tiger', 'turkey', 'turtle', 'whale', 'wolf', 'wombat', 'woodpecker', 'zebra']
+
+# Define regression function
+def predict_regression(image):
+    # Preprocess image
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150))
+    # If model expects RGB, convert to RGB
+    image = image.convert('RGB')  # Ensure image is in RGB format
+
+
+    image = np.array(image)
+    print(image.shape)
+    # Predict
+    prediction = model.predict(image[None, ...])  # Assuming single regression value
+    confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
+    return confidences
+
+
+# Create Gradio interface
+input_image = gr.Image()
+output_text = gr.Textbox(label="Predicted Value")
+interface = gr.Interface(fn=predict_regression, 
+                         inputs=input_image, 
+                         outputs=gr.Label(),
+                         examples=["images/bond.jpg", "images/cat.jpg", "images/kronk.jpgg", "images/zebra.jpg", "images/dog.jpg", "images/johnson.jpg", "images/panda.jpg" ], 
+                         description="A simple mlp classification model for image classification using a few pokemons.")
+interface.launch()

mySpiritualAnimalResNet50V2.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:85cc3afb88c4456a450772f5f47e8abe91e6613c9e8e20dd3fee2ca1ec000cd5
+size 285416811

requirements.txt

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+tensorflow