Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+import joblib  # Assuming label_encoder is saved as a .pkl file
+
+# 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)
+
+# Load label encoder
+label_encoder = joblib.load("label_encoder.pkl")  # Adjust if you have another format
+
+# 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 = torch.argmax(outputs.logits).item()
+    decoded_label = label_encoder.inverse_transform([predicted_label])[0]
+    return decoded_label
+
+# 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()