app.py

import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import os

# --- 1. Load Model from Hugging Face Hub ---

# Get the Hugging Face token from the Space's secrets
# This is crucial for accessing a private model
HF_TOKEN = os.getenv("HF_TOKEN")

# Ensure the token is set
if HF_TOKEN is None:
    raise ValueError(
        "Hugging Face token not found. Please set the HF_TOKEN secret in your Space settings."
    )

# The ID of your private model on the Hub
MODEL_ID = "breadlicker45/bilingual-large-gender-v4-test"

print(f"Loading model: {MODEL_ID}...")
try:
    # Explicitly load tokenizer and model to pass the token and trust_remote_code
    # trust_remote_code=True is needed for models with custom architectures/code
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN)
    
    model = AutoModelForSequenceClassification.from_pretrained(
        MODEL_ID,
        token=HF_TOKEN,
        trust_remote_code=True # IMPORTANT for custom models
    )
    
    # Create the pipeline using the pre-loaded model and tokenizer
    classifier = pipeline(
        "text-classification",
        model=model,
        tokenizer=tokenizer
    )
    print("Model loaded successfully!")

except Exception as e:
    # Provide a helpful error message if loading fails
    print(f"Error loading model: {e}")
    # You can display this error in the Gradio UI as well if you want
    # For now, we'll just let the Space crash with a clear log message.
    raise e

# --- 2. Define the Prediction Function ---

def classify_gender(text: str) -> dict:
    """
    Takes a string of text and returns the model's predictions
    in a format that Gradio's Label component can display.
    """
    if not text or not text.strip():
        # Handle empty or whitespace-only input gracefully
        return None 
    
    # The pipeline will run the text through the model
    # top_k=3 ensures we get scores for all 3 labels
    predictions = classifier(text, top_k=3)

    # Format the predictions into a {label: confidence} dictionary for the gr.Label component
    formatted_predictions = {p['label']: p['score'] for p in predictions}
    return formatted_predictions

# --- 3. Create the Gradio Interface ---

DESCRIPTION = """
## Bilingual Gender Classifier 
This is a demo for the private model `breadlicker45/bilingual-large-gender-v4-test`.
Enter a sentence in **English or Spanish**, and the model will predict whether the text has a male, female, or neutral connotation.
**Disclaimer:** This model, like any AI, can have biases and may not always be accurate. It is intended for demonstration purposes.
"""

ARTICLE = """
<div style='text-align: center;'>
    <p>Model based on <a href='https://huggingface.co/xlm-roberta-large' target='_blank'>XLM-RoBERTa-Large</a>, fine-tuned for gender classification.</p>
    <p>This is a private model, but you can find more public models on the <a href='https://huggingface.co/models' target='_blank'>Hugging Face Hub</a>.</p>
</div>
"""

# Define some examples for users to try
examples = [
    ["He went to the store to buy a new hammer."],
    ["La doctora le recetó un medicamento a su paciente."],
    ["The development team will present their findings tomorrow."],
    ["My sister is the best programmer I know."],
    ["El futbolista marcó el gol decisivo."],
    ["The flight crew is preparing for takeoff."]
]

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(DESCRIPTION)
    
    with gr.Row():
        with gr.Column(scale=2):
            text_input = gr.Textbox(
                lines=5, 
                label="Input Text", 
                placeholder="Enter a sentence in English or Spanish here..."
            )
            submit_btn = gr.Button("Classify Text", variant="primary")
        
        with gr.Column(scale=1):
            output_label = gr.Label(
                num_top_classes=3, 
                label="Classification Results"
            )
    
    gr.Examples(
        examples=examples,
        inputs=text_input,
        outputs=output_label,
        fn=classify_gender,
        cache_examples=True
    )
    
    gr.Markdown(ARTICLE)

    submit_btn.click(
        fn=classify_gender, 
        inputs=text_input, 
        outputs=output_label,
        api_name="classify" # You can add an API name for programmatic access
    )

# --- 4. Launch the App ---

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