import gradio as gr | |
from transformers import pipeline | |
# Load models | |
emotion_classifier = pipeline( | |
"text-classification", | |
model="bhadresh-savani/distilbert-base-uncased-emotion" | |
) | |
hate_speech_classifier = pipeline( | |
"text-classification", | |
model="Hate-speech-CNERG/dehatebert-mono-english" | |
) | |
def analyze_text(text): | |
emotion_result = emotion_classifier(text) | |
hate_result = hate_speech_classifier(text) | |
emotions = {res['label']: f"{res['score']*100:.2f}%" for res in emotion_result} | |
hate_speech = {res['label']: f"{res['score']*100:.2f}%" for res in hate_result} | |
return emotions, hate_speech | |
iface = gr.Interface( | |
fn=analyze_text, | |
inputs=gr.Textbox(lines=4, placeholder="Enter your text here..."), | |
outputs=[ | |
gr.Label(num_top_classes=6, label="Emotion Detection"), | |
gr.Label(num_top_classes=3, label="Hate Speech Detection") | |
], | |
title="🧠 Emotion & Hate Speech Detector" | |
) | |
if __name__ == "__main__": | |
iface.launch() | |