import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the model and tokenizer from Hugging Face Hub
model_name = "sanzanalora/BanglaBERT_fine-tuned_on_Ben-Sarc"  # Update with your model repo name
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define a function to perform sarcasm detection
def detect_sarcasm(input_text):
    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
    outputs = model(**inputs)
    prediction = outputs.logits.argmax(-1).item()
    return "Sarcastic" if prediction == 1 else "Not Sarcastic"

# Example Bengali sarcasm sentences
sarcasm_examples = [
    ["ও হ্যাঁ, তুমি তো অনেক বড় বিজ্ঞানী!"],
    ["অবশ্যই, এই কাজটা খুবই সহজ ছিল।"],
    ["তুমি এত দেরি করো না, সবসময়ই সময়মত আসো!"]
]

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Bengali Sarcasm Detection")
    gr.Markdown("**Upload your sarcasm detection model and test it with this Gradio interface.**")

    with gr.Tab("Sarcasm Detection"):
        gr.Examples(
            sarcasm_examples,
            inputs=gr.Textbox(label="Enter Bengali Text", lines=2),
            outputs=gr.Textbox(label="Prediction")
        )

    input_text = gr.Textbox(label="Enter your Bengali text for sarcasm detection", lines=2)
    output_text = gr.Textbox(label="Sarcasm Detection Result", lines=2)

    submit_btn = gr.Button("Detect Sarcasm")
    submit_btn.click(detect_sarcasm, inputs=input_text, outputs=output_text)

demo.launch()