app.py

import gradio as gr
import numpy as np
import urllib
from keras.preprocessing import image
from keras.models import load_model

# Load the pre-trained model
model = load_model('my_model.h5')
CATEGORIES = ("NSFW", "SFW")
def classify_image(img):
    # Preprocess the input image
    img = image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img /= 255.0
    
    # Use the model to make a prediction
    prediction = model.predict(img)
    print(prediction)
    # Map the predicted class to a label
    if prediction[0][0] >= 0.5:
        label = "SFW"
    else:
        label = "NSFW"
    
    return label

def classify_url(url):
    # Load the image from the URL
    response = urllib.request.urlopen(url)
    img = image.load_img(response, target_size=(224, 224))
    
    return classify_image(img)

# Define the GRADIO input interface
#inputs = gr.inputs.Image(shape=(224, 224, 3))

# Define the GRADIO output interface
#outputs = gr.outputs.Textbox(lines=1, default="SFW")

# Define the GRADIO app
app = gr.Interface(classify_image, gr.Image(shape=(224, 224)), outputs=gr.Label(label="Type of Image"), allow_flagging="never", title="NSFW/SFW Classifier")

# Start the GRADIO app
app.launch()