app.py

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()