Create app.py

app.py

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+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()