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Abuse_HateSpe_Profanity_Detector_BERT.py

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+import os
+import torch
+import gradio as gr
+from transformers import pipeline
+
+# Load the models
+Abuse_detector = pipeline("text-classification", model="Hate-speech-CNERG/english-abusive-MuRIL")
+Hate_Speech_detector = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-hate-latest")
+Profanity_detector = pipeline("text-classification", model="tarekziade/pardonmyai")
+
+# Function to classify based on the user's choice
+def classify_text(text, detection_type):
+    if detection_type == "Abuse":
+        result = Abuse_detector(text)
+        if result[0]['label'] == 'LABEL_0':
+            result[0]['label'] = "Not-abusive"
+        elif result[0]['label'] == 'LABEL_1':
+            result[0]['label'] = "Abusive"
+    elif detection_type == "Hate Speech":
+        result = Hate_Speech_detector(text)
+
+    elif detection_type == "Profanity":
+        result = Profanity_detector(text)
+
+    return result[0]
+
+# Gradio interface
+interface = gr.Interface(
+    fn=classify_text,
+    inputs=[gr.Textbox(label="Enter text"), gr.Radio(["Abuse", "Hate Speech", "Profanity"], label="Detection Type")],
+    outputs="json",
+    title="Abuse, Hate Speech, and Profanity Detection",
+    description="Enter text and select the type of detection you want."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch(share=True)

recquirements.txt

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+transformers
+torch
+gradio