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import gradio as gr |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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import easyocr |
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from PIL import Image |
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import numpy as np |
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SARCASM_MODEL_NAME = "j-hartmann/emotion-english-distilroberta-base" |
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sarcasm_labels = ["not sarcastic", "sarcastic"] |
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sarcasm_tokenizer = AutoTokenizer.from_pretrained(SARCASM_MODEL_NAME) |
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sarcasm_model = AutoModelForSequenceClassification.from_pretrained(SARCASM_MODEL_NAME) |
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HATE_MODEL_NAME = "cardiffnlp/twitter-roberta-base-hate-multiclass-latest" |
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hate_labels = [ |
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"sexism", |
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"racism", |
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"disability", |
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"sexual_orientation", |
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"religion", |
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"other", |
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"not_hate" |
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] |
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hate_tokenizer = AutoTokenizer.from_pretrained(HATE_MODEL_NAME) |
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hate_model = AutoModelForSequenceClassification.from_pretrained(HATE_MODEL_NAME) |
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reader = easyocr.Reader(['en'], gpu=False) |
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def extract_text(image): |
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if isinstance(image, Image.Image): |
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image = np.array(image) |
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texts = reader.readtext(image, detail=0) |
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return ' '.join(texts) |
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def detect_sarcasm(text): |
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inputs = sarcasm_tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
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with torch.no_grad(): |
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outputs = sarcasm_model(**inputs) |
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probs = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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pred = torch.argmax(probs).item() |
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conf = float(probs[0][pred]) |
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return sarcasm_labels[pred], conf |
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def classify_hate(text): |
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inputs = hate_tokenizer(text, return_tensors="pt", truncation=True, padding=True) |
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with torch.no_grad(): |
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outputs = hate_model(**inputs) |
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probs = torch.nn.functional.softmax(outputs.logits, dim=-1) |
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pred = torch.argmax(probs).item() |
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conf = float(probs[0][pred]) |
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return hate_labels[pred], conf |
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def respond(chat_history, user_text, user_image): |
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if user_image is not None: |
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extracted_text = extract_text(user_image) |
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if extracted_text.strip(): |
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text_to_analyze = extracted_text |
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elif user_text and user_text.strip(): |
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text_to_analyze = user_text.strip() |
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else: |
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chat_history.append(("User", "")) |
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chat_history.append(("Bot", "Please provide text or an image with readable text.")) |
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return chat_history, None, None |
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else: |
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text_to_analyze = user_text.strip() |
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sarcasm_label, sarcasm_conf = detect_sarcasm(text_to_analyze) |
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if sarcasm_label == "sarcastic": |
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bot_response = f"Sarcasm detected (Confidence: {sarcasm_conf:.2f}). Hate speech detection skipped." |
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else: |
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hate_label, hate_conf = classify_hate(text_to_analyze) |
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bot_response = ( |
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f"Hate Speech Category: {hate_label} (Confidence: {hate_conf:.2f})\n" |
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f"Message: \"{text_to_analyze}\"" |
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) |
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chat_history.append(("User", text_to_analyze)) |
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chat_history.append(("Bot", bot_response)) |
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return chat_history, None, None |
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with gr.Blocks() as demo: |
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gr.Markdown("# Cyber Bully Detection System") |
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chat_history = gr.State([]) |
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chatbot = gr.Chatbot() |
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txt = gr.Textbox(show_label=False, placeholder="Type your message here and press Enter") |
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img = gr.Image(source="upload", type="pil", label="Upload Screenshot (optional)") |
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clear_btn = gr.Button("Clear Chat") |
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txt.submit(respond, [chatbot, txt, img], [chatbot, txt, img]) |
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submit_img_btn = gr.Button("Analyze Image") |
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submit_img_btn.click(respond, [chatbot, txt, img], [chatbot, txt, img]) |
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clear_btn.click(lambda: ([], None, None), None, [chatbot, txt, img]) |
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if __name__ == "__main__": |
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demo.launch() |
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