app.py

import gradio as gr

# Load the model from Hugging Face
model_name = "comment-toxicity-analyzer/toxicity_det"
api_key = "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"
interface = gr.load(model_name, src="spaces", hf_api_key=api_key)

def score_comment(comment):
    """
    This function will take in a comment and then pass it through a prediction pipeline.
    """
    # Make predictions using the Gradio interface
    results = interface.predict([comment])

    # Process the results
    text = ""
    for idx, col in enumerate(["toxic", "severe_toxic", "obscene", "threat", "insult", "identity_hate"]):
        text += f"{col}: {results[0][col] > 0.40}\n"

    return text

# Define the Gradio interface
interface = gr.Interface(
    fn=score_comment,
    inputs=gr.inputs.Textbox(lines=2, placeholder="Comment to score"),
    outputs="text"
)

# Launch the interface on a local server
# if __name__ == "__main__":
interface.launch(share=True)