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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_character(img):
img = Image.fromarray(img.astype('uint8'), 'RGB') # Ensure the image is in RGB
img = img.resize((224, 224)) # Resize the image to the input size of the model
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 match 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 = ['Chopper', 'Nami', 'Ruffy', 'Sanji', 'Usopp', 'Zoro'] # Character names as per your dataset
return {classes[i]: float(prediction[0][i]) for i in range(len(classes))} # Return the prediction in a dictionary format
# Define Gradio interface
interface = gr.Interface(
fn=predict_character,
inputs=gr.Image(), # Gradio handles resizing automatically based on the model input
outputs=gr.Label(num_top_classes=6), # Show top 3 predictions
title="One Piece Character Classifier",
description="Upload an image of a One Piece character and the classifier will predict which character it is."
)
# Launch the interface
interface.launch()
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