import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
from transformers import AutoModelForSequenceClassification
import os
from huggingface_hub import login

token = os.getenv("HF_TOKEN")  # HF_TOKEN should be set in your environment
login(token=token)

model_path = "SivaMallikarjun/multi-lang-rl-model"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
model_path = "SivaMallikarjun/multi-lang-rl-model"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

def classify_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    outputs = model(**inputs)
    prediction = torch.argmax(outputs.logits, dim=1).item()
    label = "Correct" if prediction == 1 else "Incorrect"
    return label

iface = gr.Interface(fn=classify_text, 
                     inputs="text", 
                     outputs="text",
                     title="Multi-Language RL Text Classifier")

if __name__ == "__main__":
    iface.launch()