API case bn

app.py

@@ -2,8 +2,8 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load model
-model_id = "Rerandaka/Cild_safety_bigbird"
+# Load model and tokenizer
+model_id = "Rerandaka/Child_safty_bigbird_1"
 tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
 model = AutoModelForSequenceClassification.from_pretrained(model_id)
 
@@ -15,14 +15,19 @@ def classify(text):
         predicted_class = torch.argmax(logits, dim=1).item()
     return str(predicted_class)
 
-# Gradio interface with a named API
-demo = gr.Interface(
-    fn=classify,
-    inputs=gr.Textbox(label="Text Input"),
-    outputs=gr.Textbox(label="Prediction"),
-    title="Child-Safety Text Classifier",
-    description="This model detects unsafe or inappropriate text for children."
-)
+# Use Blocks to define interface and API
+with gr.Blocks() as demo:
+    gr.Markdown("## Child-Safety Text Classifier\nThis model detects unsafe or inappropriate text for children.")
 
-# ✅ Launch with API endpoint
-demo.launch(api_name="predict")
+    with gr.Row():
+        input_box = gr.Textbox(label="Enter text to classify")
+        output_box = gr.Textbox(label="Prediction")
+
+    btn = gr.Button("Submit")
+    btn.click(fn=classify, inputs=input_box, outputs=output_box)
+
+    # ✅ Named API endpoint
+    demo.load(fn=classify, inputs=input_box, outputs=output_box, api_name="predict")
+
+# Launch space
+demo.launch()