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import gradio as gr
from transformers import pipeline
# Load the model
model_name = "maiurilorenzo/misogyny-detection-it"
classifier = pipeline("text-classification", model=model_name)
# Define the prediction function
def detect_misogyny(text):
result = classifier(text)
label = result[0]["label"]
score = result[0]["score"]
label_readable = "Misogynistic" if label == "LABEL_1" else "Non-Misogynistic"
return f"Label: {label_readable} (Confidence: {score:.2f})"
# Create the Gradio interface
demo = gr.Interface(
fn=detect_misogyny,
inputs=gr.Textbox(lines=3, placeholder="Enter Italian text here..."),
outputs="text",
title="Misogyny Detection in Italian",
description="This demo uses a fine-tuned BERT model to detect misogynistic content in Italian text. Enter a phrase or sentence, and the model will classify it as 'Misogynistic' or 'Non-Misogynistic' along with a confidence score.",
article="""
### About the Model
This model is fine-tuned on the AMI (Automatic Misogyny Identification) dataset for binary classification of misogynistic content in Italian.
- **Labels:**
- `1`: Misogynistic
- `0`: Non-Misogynistic
- **Source Model:** [dbmdz/bert-base-italian-xxl-uncased](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased)
"""
)
demo.launch()
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