File size: 2,042 Bytes
22ecc00 6606691 22ecc00 7d75000 6606691 22ecc00 4d69047 22ecc00 b80445d 22ecc00 b80445d 22ecc00 b80445d 22ecc00 b80445d 22ecc00 6606691 22ecc00 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
import gradio as gr
import numpy as np
import urllib.request
from PIL import Image
from io import BytesIO
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
# Load the model
model = load_model("my_model.h5", compile=False)
# Common prediction function
def classify_pil_image(pil_img):
img = pil_img.resize((224, 224))
img = image.img_to_array(img)
img = np.expand_dims(img, axis=0)
img = img / 255.0
prediction = model.predict(img)[0]
return {
"CART": float(prediction[0]),
"NSFW": float(prediction[1]),
"SFW": float(prediction[2])
}
# From file input (or example)
def classify_uploaded_image(file):
try:
pil_img = Image.fromarray(file).convert("RGB")
return classify_pil_image(pil_img)
except Exception as e:
return {"error": f"Upload error: {str(e)}"}
# From URL input
def classify_from_url(url):
try:
response = urllib.request.urlopen(url)
img = Image.open(BytesIO(response.read())).convert("RGB")
return classify_pil_image(img)
except Exception as e:
return {"error": f"URL error: {str(e)}"}
# Example images for file-based interface
examples = [[f"example{i}.jpg"] for i in range(1, 9)]
# Upload tab (classic layout with examples)
upload_interface = gr.Interface(
fn=classify_uploaded_image,
inputs=gr.Image(type="numpy", label="Upload or drag an image"),
outputs=gr.Label(num_top_classes=3, label="Prediction"),
examples=examples,
title="Simple NSFW/SFW/CART Classifier",
allow_flagging="never",
cache_examples=False
)
# URL tab (simple textbox interface)
url_interface = gr.Interface(
fn=classify_from_url,
inputs=gr.Textbox(label="Paste Image URL"),
outputs=gr.Label(num_top_classes=3, label="Prediction"),
allow_flagging="never",
cache_examples=False
)
# Tabs wrapper to combine them
gr.TabbedInterface(
[upload_interface, url_interface],
tab_names=["📤 Upload Image", "🌐 Image URL"]
).launch()
|