Create app.py

app.py

@@ -0,0 +1,24 @@
+from transformers import BertForSequenceClassification, BertTokenizerFast, pipeline
+import gradio as gr
+
+model_path = "indiaai-text-classification-model"
+model = BertForSequenceClassification.from_pretrained(model_path)
+tokenizer = BertTokenizerFast.from_pretrained(model_path)
+nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, device="cuda")
+
+def classify_text(input_text):
+    result = nlp(input_text)
+    label = result[0]['label']
+    score = result[0]['score']
+    output = f"**Prediction:** {label}\n\n**Confidence Score:** {score:.5f}"
+    return output
+
+interface = gr.Interface(
+    fn=classify_text,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your complaint"),
+    outputs=gr.outputs.Markdown(),
+    title="INDIAai CyberGuard",
+    description="Categorizes cyber complaints based on the victim, type of fraud, and other relevant parameters.",
+)
+
+interface.launch()