kiki7555/pokemon_classifier_tf.keras

How to Get Started with the Model

To use this model, you can either interact with it programmatically using the Python code below or through a web-based interface provided by Gradio.

Using Python Code

from transformers import TFAutoModelForImageClassification, AutoTokenizer
import gradio as gr

# Laden Sie das Modell und den Tokenizer von Hugging Face herunter
model = TFAutoModelForImageClassification.from_pretrained("kiki7555/pokemon_classifier_tf")
tokenizer = AutoTokenizer.from_pretrained("kiki7555/pokemon_classifier_tf")

def predict_pokemon(image):
    # Hier kannst du die Bildvorverarbeitung und -nachverarbeitung hinzufügen
    # ...

    # Vorhersage treffen
    predictions = model.predict(image)  # Hier musst du die genaue Vorverarbeitung für das Bild hinzufügen
    predicted_class = predictions.argmax()
    
    class_names = ['Charizard', 'Pikachu', 'Zapdos']
    return class_names[predicted_class]

# Gradio UI erstellen
image_input = gr.inputs.Image(shape=(128, 128))
output_text = gr.outputs.Textbox()

gr.Interface(
    fn=predict_pokemon,
    inputs=image_input,
    outputs=output_text,
    title="Pokemon Classifier",
    description="Classify images of Pokemon into three categories: Charizard, Pikachu, and Zapdos."
).launch()