Create app.py

app.py

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+import torch
+from transformers import DebertaV2Tokenizer, DebertaV2ForSequenceClassification
+import gradio as gr
+
+
+REPO_ID = "Nikpatil/Email_classifier"
+MAX_LENGTH = 256
+
+# Define your label mapping
+id2label = {0: "Incident", 1: "Request", 2: "Problem", 3: "Change"}  
+labels = list(id2label.values())
+
+# Load model & tokenizer
+tokenizer = DebertaV2Tokenizer.from_pretrained(REPO_ID)
+model = DebertaV2ForSequenceClassification.from_pretrained(REPO_ID)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# Your inference function (adapted from your own)
+def inference(text, model, tokenizer, MAX_LENGTH, id2label):
+    inputs = tokenizer(
+        text,
+        add_special_tokens=True,
+        max_length=max_length,
+        padding='max_length',
+        truncation=True,
+        return_tensors='pt'
+    )
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
+    predicted_class_id = torch.argmax(probs).item()
+    predicted_class = id2label[predicted_class_id]
+
+    return f"Predicted: {predicted_class}", {id2label[i]: float(probs[i]) for i in range(len(probs))}
+
+# Gradio interface
+demo = gr.Interface(
+    fn=lambda text: inference(text, model, tokenizer, MAX_LENGTH, id2label),
+    inputs=gr.Textbox(lines=8, placeholder="Paste your email here..."),
+    outputs=["text", "label"],
+    title="Email Classifier (DeBERTa v2)",
+    description="Classifies emails into Incident, Request, Problem, or Change."
+)
+
+if __name__ == "__main__":
+    demo.launch()