app.py

import gradio as gr
from transformers import pipeline

# Initialize the classifiers
zero_shot_classifier = pipeline("zero-shot-classification", model="tasksource/ModernBERT-base-nli")
nli_classifier = pipeline("text-classification", model="tasksource/ModernBERT-base-nli")

if False:
    gr.load("models/answerdotai/ModernBERT-base").launch()

# Define examples
zero_shot_examples = [
    ["I absolutely love this product, it's amazing!", "positive, negative, neutral"],
    ["I need to buy groceries", "shopping, urgent tasks, leisure, philosophy"],
    ["The sun is very bright today", "weather, astronomy, complaints, poetry"],
    ["I love playing video games", "entertainment, sports, education, business"],
    ["The car won't start", "transportation, art, cooking, literature"]
]

nli_examples = [
    ["A man is sleeping on a couch", "The man is awake"],
    ["The restaurant's waiting area is bustling, but several tables remain vacant", "The establishment is at maximum capacity"],
    ["The child is methodically arranging blocks while frowning in concentration", "The kid is experiencing joy"],
    ["Dark clouds are gathering and the pavement shows scattered wet spots", "It's been raining heavily all day"],
    ["A German Shepherd is exhibiting defensive behavior towards someone approaching the property", "The animal making noise is feline"]
]

def process_input(text_input, labels_or_premise, mode):
    if mode == "Zero-Shot Classification":
        labels = [label.strip() for label in labels_or_premise.split(',')]
        prediction = zero_shot_classifier(text_input, labels)
        results = {label: score for label, score in zip(prediction['labels'], prediction['scores'])}
        return results, ''
    else:  # NLI mode
        pred = nli_classifier([{"text": text_input, "text_pair": labels_or_premise}], return_all_scores=True)[0]
        results = {pred['label']: pred['score'] for pred in pred}
        return results, ''

def update_interface(mode):
    if mode == "Zero-Shot Classification":
        return (
            gr.update(
                label="🏷️ Categories", 
                placeholder="Enter comma-separated categories...",
            ),
            gr.update()
        )
    else:
        return (
            gr.update(
                label="🔎 Hypothesis", 
                placeholder="Enter a hypothesis to compare with the premise...",
            ),
            gr.update()
        )

def handle_example_selection(evt: gr.SelectData, mode):
    # Return the appropriate label based on the current mode
    if mode == "Zero-Shot Classification":
        return gr.update(label="🏷️ Categories")
    else:
        return gr.update(label="🔎 Hypothesis")

with gr.Blocks() as demo:
    gr.Markdown("""
    # tasksource/ModernBERT-nli demonstration
    
    This spaces uses [tasksource/ModernBERT-base-nli](https://huggingface.co/tasksource/ModernBERT-base-nli), 
    fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) 
    on tasksource classification tasks. 
    This NLI model achieves high accuracy on logical reasoning and long-context NLI, outperforming Llama 3 8B on ConTRoL and FOLIO.
    """)

    mode = gr.Radio(
        ["Zero-Shot Classification", "Natural Language Inference"],
        label="Select Mode",
        value="Zero-Shot Classification"
    )
    
    with gr.Column():
        text_input = gr.Textbox(
            label="✍️ Input Text",
            placeholder="Enter your text...",
            lines=3,
            value=zero_shot_examples[0][0]
        )
        
        labels_or_premise = gr.Textbox(
            label="🏷️ Categories",
            placeholder="Enter comma-separated categories...",
            lines=2,
            value=zero_shot_examples[0][1]
        )
        
        submit_btn = gr.Button("Submit")
        
        outputs = [
            gr.Label(label="📊 Results"),
            gr.Markdown(label="📈 Analysis", visible=False)
        ]

        with gr.Column(variant="panel") as zero_shot_examples_panel:
            examples_zero = gr.Examples(
                examples=zero_shot_examples,
                inputs=[text_input, labels_or_premise],
                label="Zero-Shot Classification Examples",
            )
    
        with gr.Column(variant="panel") as nli_examples_panel:
            examples_nli = gr.Examples(
                examples=nli_examples,
                inputs=[text_input, labels_or_premise],
                label="Natural Language Inference Examples",
            )

    def update_visibility(mode):
        return (
            gr.update(visible=(mode == "Zero-Shot Classification")),
            gr.update(visible=(mode == "Natural Language Inference"))
        )

    # Update interface on mode change
    mode.change(
        fn=update_interface,
        inputs=[mode],
        outputs=[labels_or_premise, text_input]
    )
    
    mode.change(
        fn=update_visibility,
        inputs=[mode],
        outputs=[zero_shot_examples_panel, nli_examples_panel]
    )
    
    # Handle example selection events
    examples_zero.select(
        fn=handle_example_selection,
        inputs=[mode],
        outputs=[labels_or_premise]
    )
    
    examples_nli.select(
        fn=handle_example_selection,
        inputs=[mode],
        outputs=[labels_or_premise]
    )
    
    submit_btn.click(
        fn=process_input,
        inputs=[text_input, labels_or_premise, mode],
        outputs=outputs
    )

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