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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-model_transferlearning.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,52 @@
+import gradio as gr
+import tensorflow as tf
+print(tf.__version__)
+import numpy as np
+from PIL import Image
+
+import os
+
+model_path = "pokemon-model_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+
+def predict_pokemon(image):
+    # Preprocess image
+    print(type(image))
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150))  # Resize the image to 150x150 pixels
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+    # Predict
+    prediction = model.predict(image)
+    # Convert the probabilities to rounded values
+    prediction = np.round(prediction, 2)
+
+    # Make sure the indices are correct according to your model's training
+    p_dratini = prediction[0][0]  # Probability for class 'dratini'
+    p_eevee = prediction[0][1]    # Probability for class 'eevee'
+    p_jolteon = prediction[0][2]  # Probability for class 'jolteon'
+
+    return {'dratini': p_dratini, 'eevee': p_eevee, 'jolteon': p_jolteon}
+ 
+ 
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/Dratini1.jpg", 
+              "images/Dratini2.jpg", 
+              "images/Dratini3.jpg", 
+              "images/Eevee1.jpg",
+              "images/Eevee2.jpg",
+              "images/Eevee3.jpg",
+              "images/Jolteon1.jpg",
+              "images/Jolteon2.jpg",
+              "images/Jolteon3.jpg"], 
+    description="TEST.")
+ 
+iface.launch()
+ 
+

pokemon-model_transferlearning.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57c7a246c572082a9fef5d75425767f23cfc76a10aae428276845c0c175341fd
+size 250560147