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from datasets import load_dataset |
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from transformers import pipeline |
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import gradio as gr |
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dataset = load_dataset("Koushim/processed-jigsaw-toxic-comments", split="train", streaming=True) |
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green, yellow, red = [], [], [] |
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for example in dataset: |
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score = example['toxicity'] |
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text = example['text'] |
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if score < 0.3 and len(green) < 3: |
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green.append((text, score)) |
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elif 0.3 <= score < 0.7 and len(yellow) < 3: |
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yellow.append((text, score)) |
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elif score >= 0.7 and len(red) < 3: |
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red.append((text, score)) |
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if len(green) == 3 and len(yellow) == 3 and len(red) == 3: |
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break |
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examples_html = f""" |
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### 🥰 Examples: Is your partner a Green Flag or Red Flag? |
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#### 💚 Green Flag (Wholesome vibes 🌸) |
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- {green[0][0]} (toxicity: {green[0][1]:.2f}) |
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- {green[1][0]} (toxicity: {green[1][1]:.2f}) |
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- {green[2][0]} (toxicity: {green[2][1]:.2f}) |
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#### 🟡 Yellow Flag (Eh… watch out 👀) |
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- {yellow[0][0]} (toxicity: {yellow[0][1]:.2f}) |
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- {yellow[1][0]} (toxicity: {yellow[1][1]:.2f}) |
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- {yellow[2][0]} (toxicity: {yellow[2][1]:.2f}) |
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#### ❤️ Red Flag (🚨 Run bestie, run! 🚨) |
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- {red[0][0]} (toxicity: {red[0][1]:.2f}) |
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- {red[1][0]} (toxicity: {red[1][1]:.2f}) |
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- {red[2][0]} (toxicity: {red[2][1]:.2f}) |
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""" |
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classifier = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-offensive", top_k=None) |
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def predict_flag(text): |
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preds = classifier(text)[0] |
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score = 0.0 |
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for pred in preds: |
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if pred['label'].lower() in ['toxic', 'offensive', 'abusive']: |
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score = pred['score'] |
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break |
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if score < 0.3: |
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return f"💚 **Green Flag!**\nNot toxic at all. Keep them! 🌷 (toxicity: {score:.2f})" |
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elif 0.3 <= score < 0.7: |
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return f"🟡 **Yellow Flag!**\nHmm… could be better. Watch out. 👀 (toxicity: {score:.2f})" |
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else: |
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return f"❤️ **Red Flag!**\n🚨 Yikes, that’s toxic! 🚨 (toxicity: {score:.2f})" |
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with gr.Blocks() as demo: |
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gr.Markdown("# 💌 Green Flag or Red Flag?") |
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gr.Markdown("Ever wondered if your partner’s texts are a green flag 💚 or a 🚨 red flag? Paste their messages below and let AI judge. Just for fun 😉") |
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gr.Markdown(examples_html) |
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inp = gr.Textbox(label="📩 Paste your partner's message here") |
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out = gr.Markdown(label="🧪 Verdict") |
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btn = gr.Button("👀 Check Now") |
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btn.click(fn=predict_flag, inputs=inp, outputs=out) |
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demo.launch() |
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