Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model from the Hub or local directory
+model_name = "mjpsm/recommendation-overview-classification-model"  # 🔁 Replace with your model path
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+id2label = model.config.id2label
+
+# Inference function
+def predict_tag(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_id = torch.argmax(logits, dim=1).item()
+    predicted_label = id2label[predicted_class_id]
+    return predicted_label
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict_tag,
+    inputs=gr.Textbox(lines=4, placeholder="Enter student reflection..."),
+    outputs="text",
+    title="🧠 Recommendation Overview Classifier",
+    description="Enter a student's reflection after a math game. The model will return a motivational recommendation tag.",
+    examples=[
+        "I got frustrated when I made a mistake but I didn’t give up.",
+        "I asked my classmate for help and it finally made sense.",
+        "It felt like budgeting in real life when I played that part of the game.",
+        "Even though I was confused, I tried a new strategy and it worked.",
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()