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import gradio as gr
import tensorflow as tf
from PIL import Image
import numpy as np
# Load your custom regression model
model_path = "Doggos.keras"
model = tf.keras.models.load_model(model_path)
labels = ['Bedlington_terrier', 'Bernese_mountain_dog', 'Dandie_Dinmont', 'Gordon_setter', 'Ibizan_hound', 'Norwegian_elkhound'] # corrected labels list
# Define regression function
def predict_regression(image):
# Preprocess image
image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image
image = image.resize((150, 150)).convert('RGB') # Resize the image to 150x150 and convert to RGB
image = np.array(image)
print(image.shape)
# Predict
prediction = model.predict(image[None, ...]) # Assuming single regression value
confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))}
return confidences
# Create Gradio interface
input_image = gr.Image()
output_text = gr.Textbox(label="Predicted Value")
interface = gr.Interface(fn=predict_regression,
inputs=input_image,
outputs=gr.Label(),
examples=["1.jpg","2.jpg","3.jpg","4.jpg","5.jpg","6.jpg"],
description="Wer ist ein guter Junge? Du bist ein aber ganz feiner und braver Junge! hechel hechel hechel :)")
interface.launch()
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