Update app.py

app.py

@@ -101,7 +101,42 @@ def enforce_s_u(text: str) -> str:
         return "u"
     return "s"
 
-def classify_text_stream(message, max_tokens, temperature, top_p):
+def format_classification_result(classification, tokens_per_second, processing_time):
+    if classification == "s":
+        status_emoji = "✅"
+        status_text = "SAFE"
+        status_color = "#22c55e"
+        description = "Content appears to be safe and appropriate."
+    else:
+        status_emoji = "🚫"
+        status_text = "UNSAFE"
+        status_color = "#ef4444"
+        description = "Content may contain inappropriate or harmful material."
+    
+    result_html = f"""
+    <div style="text-align: center; padding: 20px; border-radius: 12px; 
+                background: linear-gradient(135deg, #f8fafc 0%, #e2e8f0 100%); 
+                border: 2px solid {status_color}; margin: 10px 0;">
+        <div style="font-size: 48px; margin-bottom: 10px;">{status_emoji}</div>
+        <div style="font-size: 24px; font-weight: bold; color: {status_color}; margin-bottom: 8px;">
+            {status_text}
+        </div>
+        <div style="font-size: 16px; color: #64748b; margin-bottom: 15px;">
+            {description}
+        </div>
+        <div style="display: flex; justify-content: center; gap: 20px; font-size: 14px; color: #475569;">
+            <span>⚡ {tokens_per_second:.1f} tok/s</span>
+            <span>⏱️ {processing_time:.2f}s</span>
+        </div>
+    </div>
+    """
+    return result_html
+
+def classify_text_stream(message, max_tokens, temperature, top_p, progress=gr.Progress()):
+    if not message.strip():
+        return format_classification_result("s", 0, 0)
+    
+    progress(0, desc="Preparing classification...")
     text = build_prompt(message)
     inputs = tokenizer([text], return_tensors="pt").to(model.device)
     do_sample = bool(temperature and temperature > 0.0)
@@ -114,6 +149,7 @@ def classify_text_stream(message, max_tokens, temperature, top_p):
         eos_token_id=tokenizer.eos_token_id,
         pad_token_id=tokenizer.eos_token_id
     )
+    
     try:
         streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True, skip_prompt=True)
     except TypeError:
@@ -127,6 +163,9 @@ def classify_text_stream(message, max_tokens, temperature, top_p):
     partial_text = ""
     token_count = 0
     start_time = None
+    
+    progress(0.3, desc="Processing content...")
+    
     with torch.inference_mode():
         thread.start()
         try:
@@ -135,7 +174,7 @@ def classify_text_stream(message, max_tokens, temperature, top_p):
                     start_time = time.time()
                 partial_text += chunk
                 token_count += 1
-                yield partial_text
+                progress(0.3 + (token_count / max_tokens) * 0.6, desc="Analyzing...")
         finally:
             thread.join()
 
@@ -143,48 +182,155 @@ def classify_text_stream(message, max_tokens, temperature, top_p):
     end_time = time.time() if start_time else time.time()
     duration = max(1e-6, end_time - start_time)
     tps = token_count / duration if duration > 0 else 0.0
-    yield f"{final_label}\n\n⚡ Speed: {tps:.2f} tokens/s"
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Multilingual Content Moderation Classifier")
-    gr.Markdown("Enter any text to classify it as safe ('s') or unsafe ('u').")
     
-    with gr.Row():
-        with gr.Column(scale=2):
-            text_input = gr.Textbox(
-                label="Text to Classify", 
-                lines=5, 
-                placeholder="Enter text in any language..."
-            )
-            submit_button = gr.Button("Classify", variant="primary")
+    progress(1.0, desc="Complete!")
+    
+    return format_classification_result(final_label, tps, duration)
+
+custom_css = """
+.main-container {
+    max-width: 1200px !important;
+    margin: 0 auto !important;
+}
+
+.header-section {
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+    padding: 2rem;
+    border-radius: 16px;
+    margin-bottom: 2rem;
+    color: white;
+    text-align: center;
+}
+
+.classification-panel {
+    background: white;
+    border-radius: 16px;
+    padding: 2rem;
+    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1);
+    border: 1px solid #e2e8f0;
+}
+
+.example-card {
+    transition: transform 0.2s ease;
+}
+
+.example-card:hover {
+    transform: translateY(-2px);
+}
+
+.gradio-container {
+    font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif;
+}
+
+.input-section {
+    background: #f8fafc;
+    border-radius: 12px;
+    padding: 1.5rem;
+    border: 1px solid #e2e8f0;
+}
+"""
+
+with gr.Blocks(css=custom_css, title="AI Content Moderator", theme=gr.themes.Soft()) as demo:
+    with gr.Column(elem_classes="main-container"):
+        gr.HTML("""
+            <div class="header-section">
+                <h1 style="font-size: 2.5rem; margin-bottom: 0.5rem; font-weight: 700;">
+                    🛡️ AI Content Moderator
+                </h1>
+                <p style="font-size: 1.2rem; opacity: 0.9; margin: 0;">
+                    Advanced multilingual content classification powered by AI
+                </p>
+            </div>
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=3):
+                with gr.Group(elem_classes="input-section"):
+                    gr.Markdown("### 📝 Content Analysis")
+                    text_input = gr.Textbox(
+                        label="Text to Analyze",
+                        placeholder="Enter any text in any language for content moderation analysis...",
+                        lines=6,
+                        max_lines=10,
+                        show_label=False
+                    )
+                    
+                    with gr.Row():
+                        classify_btn = gr.Button(
+                            "🔍 Analyze Content", 
+                            variant="primary",
+                            size="lg",
+                            scale=2
+                        )
+                        clear_btn = gr.Button(
+                            "🗑️ Clear",
+                            variant="secondary",
+                            size="lg",
+                            scale=1
+                        )
+            
+            with gr.Column(scale=2):
+                with gr.Group(elem_classes="classification-panel"):
+                    gr.Markdown("### 📊 Classification Result")
+                    result_display = gr.HTML(
+                        value=format_classification_result("s", 0, 0),
+                        label="Result"
+                    )
         
-        with gr.Column(scale=1):
-            text_output = gr.Textbox(label="Classification Result", interactive=False)
-            with gr.Accordion("Advanced Settings", open=False):
+        with gr.Accordion("⚙️ Advanced Configuration", open=False):
+            with gr.Row():
                 max_tokens_slider = gr.Slider(
-                    minimum=1, maximum=4, value=1, step=1, label="Max New Tokens"
+                    minimum=1, maximum=10, value=3, step=1,
+                    label="Max Tokens",
+                    info="Maximum number of tokens to generate"
                 )
                 temp_slider = gr.Slider(
-                    minimum=0.0, maximum=1.0, value=0.0, step=0.1, label="Temperature"
+                    minimum=0.0, maximum=1.0, value=0.1, step=0.1,
+                    label="Temperature",
+                    info="Controls randomness in generation"
                 )
                 top_p_slider = gr.Slider(
-                    minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"
+                    minimum=0.1, maximum=1.0, value=0.95, step=0.05,
+                    label="Top-p",
+                    info="Nucleus sampling parameter"
                 )
+        
+        gr.Markdown("### 💡 Try These Examples")
+        
+        example_data = [
+            ["Hello, how are you today? I hope you're having a wonderful time!"],
+            ["I hate you and I will find you and hurt you badly."],
+            ["C'est une belle journée pour apprendre la programmation et l'intelligence artificielle."],
+            ["I can't take this anymore. I want to end everything and disappear forever."],
+            ["¡Hola! Me encanta aprender nuevos idiomas y conocer diferentes culturas."],
+            ["You're absolutely worthless and nobody will ever love someone like you."]
+        ]
+        
+        examples = gr.Examples(
+            examples=example_data,
+            inputs=text_input,
+            examples_per_page=6
+        )
+        
+        gr.Markdown("""
+        ---
+        <div style="text-align: center; padding: 1rem; color: #64748b; font-size: 0.9rem;">
+            <p><strong>🌍 Multilingual Support:</strong> English, Spanish, French, German, and many more languages</p>
+            <p><strong>🚀 Real-time Analysis:</strong> Fast content classification with detailed feedback</p>
+            <p><strong>🔒 Privacy First:</strong> All processing happens locally on your machine</p>
+        </div>
+        """)
 
-    gr.Examples(
-        examples=[
-            ["Hello, how are you today?"],
-            ["I will find you and hurt you."],
-            ["C'est une belle journée pour apprendre le codage."],
-            ["I want to die."],
-        ],
-        inputs=text_input
-    )
-
-    submit_button.click(
+    classify_btn.click(
         fn=classify_text_stream,
         inputs=[text_input, max_tokens_slider, temp_slider, top_p_slider],
-        outputs=text_output
+        outputs=result_display,
+        show_progress=True
+    )
+    
+    clear_btn.click(
+        fn=lambda: ("", format_classification_result("s", 0, 0)),
+        outputs=[text_input, result_display]
     )
 
 if __name__ == "__main__":
@@ -193,4 +339,10 @@ if __name__ == "__main__":
             **tokenizer(["Hi"], return_tensors="pt").to(model.device),
             max_new_tokens=1, do_sample=False, use_cache=True
         )
-    demo.queue(max_size=32).launch()
+    print("🚀 Starting AI Content Moderator...")
+    demo.queue(max_size=64, concurrency_count=4).launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
+    )