Update app.py

app.py

@@ -6,37 +6,128 @@ from transformers import (
 )
 
 MODEL_NAME = "duclo90/results"
-
 tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
-
 model.eval()
 
 def classify_text(text):
+    if not text.strip():
+        return "⚠️ Please enter some text to analyze."
+    
     inputs = tokenizer(
         text,
         return_tensors="pt",
         truncation=True,
         max_length=512
     )
-
     with torch.no_grad():
         outputs = model(**inputs)
         probs = torch.softmax(outputs.logits, dim=-1)
         score, pred = torch.max(probs, dim=-1)
-
+    
     label = model.config.id2label[pred.item()]
-    confidence = round(score.item(), 3)
+    confidence = round(score.item() * 100, 1)
+    
+    # Format output with icons and styling
+    icon = "👤" if label.lower() == "human" else "🤖"
+    
+    result = f"""
+    ### {icon} Classification Result
+    
+    **Prediction:** {label}-Written Text
+    
+    **Confidence:** {confidence}%
+    
+    {'🟦' * int(confidence // 10)} {confidence}%
+    """
+    
+    return result
+
+# Custom CSS for modern look
+custom_css = """
+#component-0 {
+    max-width: 900px;
+    margin: auto;
+    padding: 20px;
+}
+
+.gradio-container {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+    font-family: 'Inter', sans-serif;
+}
+
+#title {
+    text-align: center;
+    color: white;
+    font-size: 2.5em;
+    font-weight: 700;
+    margin-bottom: 10px;
+    text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
+}
+
+#description {
+    text-align: center;
+    color: rgba(255,255,255,0.9);
+    font-size: 1.1em;
+    margin-bottom: 30px;
+}
+
+.input-textarea, .output-markdown {
+    border-radius: 15px !important;
+    border: 2px solid rgba(255,255,255,0.3) !important;
+    background: rgba(255,255,255,0.95) !important;
+    box-shadow: 0 8px 32px rgba(0,0,0,0.1) !important;
+}
+
+button {
+    background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%) !important;
+    border: none !important;
+    border-radius: 10px !important;
+    padding: 12px 30px !important;
+    font-weight: 600 !important;
+    font-size: 16px !important;
+    box-shadow: 0 4px 15px rgba(0,0,0,0.2) !important;
+    transition: transform 0.2s !important;
+}
+
+button:hover {
+    transform: translateY(-2px) !important;
+    box-shadow: 0 6px 20px rgba(0,0,0,0.3) !important;
+}
 
-    return f"Prediction: {label} (Confidence: {confidence})"
+.footer {
+    display: none !important;
+}
+"""
 
+# Example texts
+examples = [
+    ["The sun set over the horizon, painting the sky in brilliant shades of orange and pink. I sat there, mesmerized by nature's beauty."],
+    ["According to recent data analysis, the implementation of artificial intelligence systems has increased operational productivity by approximately 40 percent across various industries."],
+    ["Can't believe how amazing that concert was last night! The energy in the crowd was absolutely electric 🎸✨"],
+]
 
+# Create interface with modern theme
 iface = gr.Interface(
     fn=classify_text,
-    inputs=gr.Textbox(lines=6, placeholder="Enter text here..."),
-    outputs="text",
-    title="Human vs Machine Text Classifier",
-    description="Classifies text as human-written or machine-generated using a fine-tuned BERT model."
+    inputs=gr.Textbox(
+        lines=8, 
+        placeholder="✍️ Paste or type text here to analyze...",
+        label="Enter Text",
+        elem_id="input-text"
+    ),
+    outputs=gr.Markdown(label="Analysis Result"),
+    title="<div id='title'>🔍 AI Text Classifier</div>",
+    description="<div id='description'>Detect whether text is human-written or AI-generated using advanced NLP</div>",
+    examples=examples,
+    theme=gr.themes.Soft(
+        primary_hue="purple",
+        secondary_hue="pink",
+        neutral_hue="slate",
+        font=gr.themes.GoogleFont("Inter")
+    ),
+    css=custom_css,
+    flagging_mode="never"
 )
 
-iface.launch()
+iface.launch()