Upload 3 files

.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
+gym_equipment_transferlearning.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
+
+# Lade dein Modell (hier als Beispiel die Keras .h5 Datei)
+model = tf.keras.models.load_model('pokemon_model.keras')
+
+# Klassennamen, sollten deinem Dataset entsprechen
+class_names = ['Jolteon', 'Kakuna', 'Mr. Mime']
+
+def classify_image(image):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    img = image.resize((150, 150))
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)  # Erstelle einen Batch
+    predictions = model.predict(img_array)
+    predicted_class = class_names[np.argmax(predictions[0])]
+    confidence = np.max(predictions[0])
+    return {predicted_class: float(confidence)}
+
+
+image_input = gr.Image()  # Entferne den `shape` Parameter
+label = gr.Label(num_top_classes=3)
+
+iface = gr.Interface(
+    fn=classify_image, 
+    inputs=image_input, 
+    outputs=label,
+    title='Pokémon Classifier',
+             description='Upload an image of Jolteon, Kakuna, Mr. Mime and the classifier will tell you which one it is and the confidence level of the prediction.').launch()
+
+iface.launch()

gym_equipment_transferlearning.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a245b55cb580c32ec74cd3c6d2a8a3bd3379789839343fc2e353e92c806b3985
+size 250584792

requirements.txt

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+tensorflow