Create app.py

Fetch the model weights from the hugging face model repo

app.py

@@ -0,0 +1,24 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel
+import torch
+
+# Load tokenizer from the uploaded HF repo
+tokenizer = AutoTokenizer.from_pretrained("PVK-Varma/DistilBERT_Banking")
+
+# Load base model and LoRA weights from HF repo
+base_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
+model = PeftModel.from_pretrained(base_model, "PVK-Varma/DistilBERT_Banking")
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        predicted_class_id = predictions.argmax().item()
+        confidence = predictions.max().item()
+    return f"Predicted class: {predicted_class_id}, Confidence: {confidence:.4f}"
+
+# Create Gradio interface
+iface = gr.Interface(fn=predict, inputs="text", outputs="text", title="DistilBERT Text Classifier")
+iface.launch()