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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 = "pokemon.keras"
model = tf.keras.models.load_model(model_path)
model.summary() # Check if the model architecture loaded matches the expected one
labels = ['Abra', 'Aerodactyl', 'Alakazam', 'Arbok', 'Arcanine']
# 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))
# If model expects RGB, convert to RGB
image = image.convert('RGB') # Ensure image is in RGB format
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=["images/abra.gif", "images/Aerodactyl.png", "images/Alakazam.png", "images/arbok.jpg", "images/Arcanine.png" ],
description="A simple mlp classification model for image classification using a few pokemons.")
interface.launch()
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