app.py

import gradio as gr
# BE EXPLICIT: Import the specific model class we need
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import os

# Get the Hugging Face token from the Space's secrets
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN is None:
    raise ValueError("Hugging Face token not found. Please set the HF_TOKEN secret in your Space settings.")

MODEL_ID = "breadlicker45/multilingual-bert-gender-classification"
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

print(f"Loading model: {MODEL_ID}...")
try:
    # Tokenizer can still be loaded automatically
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, token=HF_TOKEN, trust_remote_code=True)
    
    model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID, token=HF_TOKEN, trust_remote_code=True)
    
    # Move the model to the selected device
    model.to(device)
    print("Model loaded successfully!")

except Exception as e:
    print(f"Error loading model: {e}")
    raise e

# --- 2. Define the Manual Prediction Function ---
# (This function is already correct and does not need changes)
def classify_gender(text: str) -> dict:
    if not text or not text.strip():
        return None

    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    inputs = {k: v.to(device) for k, v in inputs.items()}

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

    probabilities = torch.nn.functional.softmax(logits, dim=-1)
    scores = probabilities.squeeze().tolist()
    
    results = {}
    for i, score in enumerate(scores):
        label_name = model.config.id2label[i]
        results[label_name] = score
        
    return results

# --- 3. Create the Gradio Interface ---
# (This part remains the same)

DESCRIPTION = """
## Bilingual Gender Classifier 
This is a demo for the model `breadlicker45/multilingual-bert-gender-classification`.
Enter a sentence and the model will predict whether the text has a male, female, or non-binary.
**Disclaimer:** This model, like any AI, can have biases and may not always be accurate.
"""

ARTICLE = """
"""

examples = [
    ["this is a test."] 
]

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 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"
    )

# --- 4. Launch the App ---
if __name__ == "__main__":
    demo.launch()