app.py

# 1. Imports
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# 2. Constants
MODEL_ID = "phirni/iab-url-classifier"

# 3. Load tokenizer + model (ONCE, at startup)
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
model.eval()

# 4. Inference function (THIS is where classify() goes)
def classify(text, threshold, top_n):
    inputs = tokenizer(
        text,
        truncation=True,
        padding=True,
        return_tensors="pt"
    )

    with torch.no_grad():
        logits = model(**inputs).logits
        probs = torch.sigmoid(logits)[0]

    label_scores = {
        model.config.id2label[i]: float(probs[i])
        for i in range(len(probs))
        if probs[i] >= threshold
    }

    sorted_labels = sorted(
        label_scores.items(),
        key=lambda x: x[1],
        reverse=True
    )

    return dict(sorted_labels[:top_n])

# 5. Gradio UI (fn points to classify)
demo = gr.Interface(
    fn=classify,
    inputs=[
        gr.Textbox(label="URL or Page Text"),
        gr.Slider(0, 1, value=0.6, step=0.05, label="Confidence Threshold"),
        gr.Slider(1, 10, value=5, step=1, label="Top N Categories"),
    ],
    outputs=gr.Label(num_top_classes=10),
    title="IAB URL Classifier"
)

# 6. Launch
demo.launch()