Update app.py

app.py

@@ -2,10 +2,10 @@ from datasets import load_dataset
 from transformers import pipeline
 import gradio as gr
 
-# Load Jigsaw dataset (streaming for large size)
+# Load dataset
 dataset = load_dataset("Koushim/processed-jigsaw-toxic-comments", split="train", streaming=True)
 
-# Fetch sample comments with low, medium, high toxicity
+# Sample examples
 low, medium, high = [], [], []
 for example in dataset:
     score = example['toxicity']
@@ -20,46 +20,43 @@ for example in dataset:
         break
 
 examples_html = f"""
-### 🔷 Examples of Toxicity Levels
+### 🧪 Examples of Toxicity Levels
 
-**🟢 Low Toxicity**
+#### 🔷 Low Toxicity
 - {low[0][0]} (score: {low[0][1]:.2f})
 - {low[1][0]} (score: {low[1][1]:.2f})
 - {low[2][0]} (score: {low[2][1]:.2f})
 
-**🟡 Medium Toxicity**
+#### 🟠 Medium Toxicity
 - {medium[0][0]} (score: {medium[0][1]:.2f})
 - {medium[1][0]} (score: {medium[1][1]:.2f})
 - {medium[2][0]} (score: {medium[2][1]:.2f})
 
-**🔴 High Toxicity**
+#### 🔴 High Toxicity
 - {high[0][0]} (score: {high[0][1]:.2f})
 - {high[1][0]} (score: {high[1][1]:.2f})
 - {high[2][0]} (score: {high[2][1]:.2f})
 """
 
-# Load a toxicity/offensive detection pipeline
-classifier = pipeline(
-    "text-classification", 
-    model="cardiffnlp/twitter-roberta-base-offensive",
-    top_k=None
-)
+# Load toxicity detection pipeline
+classifier = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-offensive", top_k=None)
 
 def predict_toxicity(text):
-    preds = classifier(text)
-    result_str = ""
+    preds = classifier(text)[0]
+    results = []
     for pred in preds:
-        result_str += f"**{pred['label']}**: {pred['score']:.2f}\n"
-    return result_str.strip()
+        label = pred['label']
+        score = pred['score']
+        results.append(f"**{label}**: {score:.2f}")
+    return "\n".join(results)
 
-# Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("# 🧹 Hate Speech & Toxicity Monitor")
     gr.Markdown("This tool shows examples of toxic comments and lets you check your own text for toxicity using a Hugging Face model.")
     gr.Markdown(examples_html)
 
     inp = gr.Textbox(label="🔷 Enter your comment")
-    out = gr.Markdown(label="Toxicity Scores")
+    out = gr.Markdown(label="🔷 Toxicity Scores")
     btn = gr.Button("Check Toxicity")
     btn.click(fn=predict_toxicity, inputs=inp, outputs=out)