Create app.py

app.py

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+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+import gradio as gr
+
+# Load model and tokenizer from local directory (same folder as app.py)
+model = BertForSequenceClassification.from_pretrained("bert")
+tokenizer = BertTokenizer.from_pretrained(".")
+
+# Ensure model runs on GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# ID to label mapping
+id2label = {0: "Select", 1: "Insert", 2: "Delete", 3: "Update", 4: "Analyse"}
+
+def classify_query(text):
+    if not text.strip():
+        return "Please enter a valid query."
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128).to(device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        prediction = torch.argmax(outputs.logits, dim=1).item()
+        label = id2label.get(prediction, "Unknown")
+    return f"🧠 Predicted Query Type: **{label}**"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=classify_query,
+    inputs=gr.Textbox(label="Enter your expense query", lines=2, placeholder="e.g., Show me all expenses from January."),
+    outputs=gr.Markdown(label="Query Type"),
+    title="💰 Expense Query Type Classifier",
+    description="This model classifies your natural language query into one of 5 SQL operation types: Select, Insert, Delete, Update, or Analyse.",
+    examples=[
+        ["Add an expense of 500 in groceries at Amazon"],
+        ["Remove last transaction from Starbucks"],
+        ["Update amount of food expense to 850"],
+        ["Kitna kharcha hua electronics par?"],
+        ["Give me analytics of travel spending"]
+    ],
+    theme="soft",
+)
+
+if __name__ == "__main__":
+    demo.launch()