app.py

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import gradio as gr


def run_inference(review_text: str) -> str:
    """
    Perform inference on the given wine review text and return the predicted wine variety.

    Args:
        review_text (str): Wine review text in the format "country [SEP] description".

    Returns:
        str: The predicted wine variety using the model's id2label mapping if available.
    """
    # Define model and tokenizer identifiers
    model_id = "spawn99/modernbert-wine-classification"
    tokenizer_id = "answerdotai/ModernBERT-base"

    # Load tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
    model = AutoModelForSequenceClassification.from_pretrained(model_id)

    # Tokenize the input text
    inputs = tokenizer(
        review_text,
        return_tensors="pt",
        padding="max_length",
        truncation=True,
        max_length=256
    )

    model.eval()
    with torch.no_grad():
        outputs = model(**inputs)
    logits = outputs.logits

    # Determine prediction and map to label if available
    pred = torch.argmax(logits, dim=-1).item()
    variety = (
        model.config.id2label.get(pred, str(pred))
        if hasattr(model.config, "id2label") and model.config.id2label
        else str(pred)
    )
    
    return variety


def predict_wine_variety(country: str, description: str) -> dict:
    """
    Combine the provided country and description, then perform inference.
    Enforces a maximum character limit of 750 on the description.

    Args:
        country (str): The country of wine origin.
        description (str): The wine review description.

    Returns:
        dict: Dictionary containing the predicted wine variety or an error message if the limit is exceeded.
    """
    # Validate description length
    if len(description) > 750:
        return {"error": "Description exceeds 750 character limit. Please shorten your input."}
    
    # Capitalize input values and format the review text accordingly.
    review_text = f"{country.capitalize()} [SEP] {description.capitalize()}"
    predicted_variety = run_inference(review_text)
    return {"Variety": predicted_variety}


if __name__ == "__main__":
    iface = gr.Interface(
        fn=predict_wine_variety,
        inputs=[
            gr.Textbox(label="Country", placeholder="Enter country of origin..."),
            gr.Textbox(label="Description", placeholder="Enter wine review description...")
        ],
        outputs=gr.JSON(label="Prediction"),
        title="Wine Variety Predictor",
        description="Predict the wine variety based on country and description."
    )
    iface.launch()