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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
+pokemon_classifier_model.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+
+# Load your custom regression model
+model_path = "pokemon_classifier_model.keras"
+model = tf.keras.models.load_model(model_path)
+
+labels = ['Dratini', 'Eevee', 'Jigglypuff']
+
+# 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)).convert('RGB') #resize the image to 28x28 and converts it to gray scale
+    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=["dratini.png", "eevee.jpg", "jigglypuff.jpg"],   
+    description="A simple mlp classification model for image classification using the pokemon dataset."
+)
+interface.launch()

pokemon_classifier_model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0f04661b4c00ee6fb9591ab3244619f3d33dd7932c647d391d0c69d878f3e3f5
+size 250560147