initial commit

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_id = "Rerandaka/child-safety-01"
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+
+# Class mapping (optional — edit as needed)
+label_map = {
+    0: "Safe / Normal",
+    1: "Inappropriate / Unsafe"
+}
+
+# Inference function
+def classify_text(text: str):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=1)
+        predicted = torch.argmax(probs, dim=1).item()
+        confidence = probs[0][predicted].item()
+    return {
+        "label": label_map.get(predicted, str(predicted)),
+        "confidence": round(confidence, 4)
+    }
+
+# Define Gradio Interface
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(label="Enter text to classify"),
+    outputs=[
+        gr.Textbox(label="Predicted Label"),
+        gr.Textbox(label="Confidence")
+    ],
+    title="Child-Safety Text Classifier",
+    description="This model detects if text content is unsafe or inappropriate for children.",
+    allow_flagging="never"
+)
+
+# Expose API endpoint explicitly
+demo.launch(api_name="predict")

requirements.txt

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+transformers
+torch
+gradio