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61fcf24 | 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 | from transformers_interpret import SequenceClassificationExplainer
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch, gradio as gr
import numpy as np
MODEL_ID = "sosohrabian/my-fine-tuned-bert"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer)
label_names = {0: "World", 1: "Sports", 2: "Business", 3: "Sci/Tech"}
device = 0 if torch.cuda.is_available() else -1
clf = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None, device=device)
def predict(text: str):
text = (text or "").strip()
if not text:
return {}
out = clf(text, truncation=True)
if isinstance(out, list) and isinstance(out[0], list):
out = out[0]
results = {}
for o in sorted(out, key=lambda x: -x["score"]):
idx = int(o["label"].split("_")[1])
results[label_names[idx]] = float(o["score"])
return results
# Build script-free HTML so it renders in Gradio pages
def explain_html(text: str) -> str:
text = (text or "").strip()
if not text:
return "<i>Enter text to see highlighted words.</i>"
atts = explainer(text) # list of (token, attribution)
toks = [t for t, _ in atts]
scores = np.abs([s for _, s in atts])
smin, smax = float(np.min(scores)), float(np.max(scores))
scores = (scores - smin) / (smax - smin + 1e-8)
spans = [
f"<span style='background: rgba(255,0,0,{0.15+0.85*s:.2f});"
f"padding:2px 3px; margin:1px; border-radius:4px; display:inline-block'>{tok}</span>"
for tok, s in zip(toks, scores)
]
return "<div style='line-height:2'>" + " ".join(spans) + "</div>"
def predict_and_explain(text: str):
return predict(text), explain_html(text)
demo = gr.Interface(
fn=predict_and_explain,
inputs=gr.Textbox(lines=3, label="Enter news headline"),
outputs=[
gr.Label(num_top_classes=4, label="Predicted topic"),
gr.HTML(label="Important-word highlights"),
],
title="AG News Topic Classifier (BERT-base)",
description="Shows predicted topic and highlights words that influenced the decision."
)
demo.launch(share=True)
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