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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()
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