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import gradio as gr | |
from PIL import Image | |
import numpy as np | |
from tensorflow.keras.preprocessing import image as keras_image | |
from tensorflow.keras.applications.resnet50 import preprocess_input | |
from tensorflow.keras.models import load_model | |
# Load your trained model | |
model = load_model('/home/user/app/mein_modell.h5') | |
def predict_pokemon(img): | |
img = Image.fromarray(img.astype('uint8'), 'RGB') # Ensure the image is in RGB | |
img = img.resize((224, 224)) # Resize the image properly using PIL | |
img_array = keras_image.img_to_array(img) # Convert the image to an array | |
img_array = np.expand_dims(img_array, axis=0) # Expand dimensions to fit model input | |
img_array = preprocess_input(img_array) # Preprocess the input as expected by ResNet50 | |
prediction = model.predict(img_array) # Predict using the model | |
classes = ['Charmeleon', 'Dewgong', 'Zubat' ] # Specific Pokémon names | |
return {classes[i]: float(prediction[0][i]) for i in range(3)} # Return the prediction | |
# Define Gradio interface | |
interface = gr.Interface(fn=predict_pokemon, | |
inputs="image", # Simplified input type | |
outputs="label", # Simplified output type | |
title="Pokémon Classifier", | |
description="Upload an image of a Pokémon and the classifier will predict its species.") | |
# Launch the interface | |
interface.launch() | |