| 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() | |