Create app.py

app.py

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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()