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import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
# Load model and tokenizer
model_name = "mmuzamilai/distilbert-review-bug-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Custom label mapping using integer keys
label_map = {
0: "Graphical Issue",
1: "Network Issue",
2: "No Bug ✅",
3: "Performance Issue"
}
# Classification function
def classify_review(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128).to(device)
with torch.no_grad():
outputs = model(**inputs)
predicted_label_id = torch.argmax(outputs.logits).item()
return label_map.get(predicted_label_id, "Unknown")
# Gradio interface
iface = gr.Interface(
fn=classify_review,
inputs=gr.Textbox(lines=2, placeholder="Enter your review..."),
outputs=gr.Label(label="Predicted Category"),
title="Review Bug Classifier"
)
iface.launch()
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