app.py

import gradio as gr
import joblib
import re
import string

# 1. Load the model and vectorizer
# Ensure 'hate_speech_model.joblib' is in the same directory
checkpoint = joblib.load('hate_speech_model.joblib')
model = checkpoint['model']
tfidf = checkpoint['tfidf']

# 2. Pre-processing function (must match the one used during training)
def clean_text(text):
    text = str(text).lower()
    text = re.sub(r'http\S+|www\S+|https\S+', '', text, flags=re.MULTILINE)
    text = re.sub(r'\@\w+|\#','', text)
    text = text.translate(str.maketrans('', '', string.punctuation))
    text = ' '.join(text.split())
    return text

# 3. Prediction function
def predict(text):
    if not text:
        return "Please enter some text."
    
    cleaned_text = clean_text(text)
    vectorized_text = tfidf.transform([cleaned_text])
    prediction = model.predict(vectorized_text)[0]
    
    # Map numerical class to label
    labels = {0: "Hate Speech", 1: "Offensive Language", 2: "Neither"}
    return labels.get(prediction, "Unknown")

# 4. Build Gradio Interface
demo = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=2, placeholder="Enter text here...", label="Input Text"),
    outputs=gr.Label(label="Classification Result"),
    title="Hate Speech Detector",
    description="This model classifies text into Hate Speech, Offensive Language, or Neither.",
    examples=[
        ["I hope you have a wonderful day!"],
        ["You are so stupid and I hate you."],
        ["That person is a complete idiot."]
    ]
)

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