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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.vgg16 import preprocess_input
from tensorflow.keras.models import load_model
# Load your trained model
model = load_model('/home/user/app/cars_VGG.h5') # Ensure this path is correct
def predict_cars(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 = ['Golf', 'RollsRoyce', 'Spark'] # Specific car models
return {classes[i]: float(prediction[0][i]) for i in range(3)} # Return the prediction
# Define Gradio interface
interface = gr.Interface(fn=predict_cars,
inputs="image", # Simplified input type
outputs="label", # Simplified output type
title="Car Classifier",
description="Upload an image of a car and the classifier will predict its species.")
# Launch the interface
interface.launch()
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