add app.py, update README.md and create requirements.txt files

README.md

@@ -1,14 +1,14 @@
 ---
-title: MagicSupport Customer Intent Classifier
-emoji: 🐢
-colorFrom: pink
-colorTo: yellow
+title: MagicSupport Intent Classifier
+emoji: 🎧
+colorFrom: violet
+colorTo: slate
 sdk: gradio
-sdk_version: 6.9.0
+sdk_version: 4.20.0
 app_file: app.py
 pinned: false
 license: apache-2.0
-short_description: This space hosts the production-grade intent classification.
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# MagicSupport Intent Classifier
+This space hosts the production-grade intent classification pipeline for the `learn-abc/magicSupport-intent-classifier` model, optimized for automated customer support routing.

app.py

@@ -0,0 +1,237 @@
+import gradio as gr
+import torch
+import logging
+import torch.nn.functional as F
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Configure professional logging
+logging.basicConfig(format='%(asctime)s | %(levelname)s | %(message)s', level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class MagicSupportClassifier:
+    """
+    Encapsulates the customer support intent classification model.
+    Engineered for dynamic label resolution and rapid inference.
+    """
+    def __init__(self, model_id: str = "learn-abc/magicSupport-intent-classifier"):
+        self.model_id = model_id
+        self.max_length = 128
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self._load_model()
+
+    def _load_model(self):
+        logger.info(f"Initializing model {self.model_id} on {self.device}...")
+        try:
+            self.tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+            self.model = AutoModelForSequenceClassification.from_pretrained(self.model_id)
+            self.model.to(self.device)
+            self.model.eval()
+            
+            # Extract number of classes dynamically
+            self.num_classes = len(self.model.config.id2label)
+            logger.info(f"Model loaded successfully with {self.num_classes} intent classes.")
+        except Exception as e:
+            logger.error(f"Failed to load model: {e}")
+            raise
+
+    def _get_iconography(self, label: str) -> str:
+        """
+        Dynamically assigns UI icons based on intent keywords.
+        Future-proofs the application against retrained label sets.
+        """
+        label_lower = label.lower()
+        if "order" in label_lower or "delivery" in label_lower or "track" in label_lower:
+            return "📦"
+        if "refund" in label_lower or "payment" in label_lower or "invoice" in label_lower or "fee" in label_lower:
+            return "💳"
+        if "account" in label_lower or "password" in label_lower or "register" in label_lower or "profile" in label_lower:
+            return "👤"
+        if "cancel" in label_lower or "delete" in label_lower or "problem" in label_lower or "issue" in label_lower:
+            return "⚠️"
+        if "contact" in label_lower or "service" in label_lower or "support" in label_lower:
+            return "🎧"
+        return "🔹"
+
+    def _format_label(self, label: str) -> str:
+        """Cleans up raw dataset labels for professional UI presentation."""
+        return label.replace("_", " ").title()
+
+    @torch.inference_mode()
+    def predict(self, text: str, top_k: int = 5):
+        if not text or not text.strip():
+            return "<div style='color: #ef4444; padding: 10px;'>⚠️ <b>Input Required:</b> Please enter a customer query.</div>", None
+
+        try:
+            inputs = self.tokenizer(
+                text.strip(),
+                return_tensors="pt",
+                truncation=True,
+                max_length=self.max_length,
+                padding=True
+            ).to(self.device)
+
+            logits = self.model(**inputs).logits
+            probs = F.softmax(logits, dim=-1).squeeze()
+            
+            if probs.dim() == 0:
+                probs = probs.unsqueeze(0)
+
+            # Cap top_k to the maximum number of available classes
+            actual_top_k = min(top_k, self.num_classes)
+            
+            top_indices = torch.topk(probs, k=actual_top_k).indices.tolist()
+            top_probs = torch.topk(probs, k=actual_top_k).values.tolist()
+            id2label = self.model.config.id2label
+
+            # Primary Prediction Formatting
+            top_intent_raw = id2label[top_indices[0]]
+            emoji = self._get_iconography(top_intent_raw)
+            clean_label = self._format_label(top_intent_raw)
+            confidence = top_probs[0] * 100
+
+            result_html = f"""
+            <h2 style='margin-bottom: 5px; display: flex; align-items: center; gap: 8px;'>{emoji} {clean_label}</h2>
+            <p style='margin-top: 0; font-size: 16px;'><b>Confidence:</b> {confidence:.1f}%</p>
+            <hr style='border-top: 1px solid var(--border-color-primary); margin: 20px 0;'/>
+            <h3 style='margin-bottom: 15px;'>📊 Top {actual_top_k} Predictions</h3>
+            """
+            
+            # HTML Progress Bars
+            for idx, prob in zip(top_indices, top_probs):
+                intent_raw = id2label[idx]
+                e = self._get_iconography(intent_raw)
+                l = self._format_label(intent_raw)
+                pct = prob * 100
+                
+                bar_html = f"""
+                <div style="margin-bottom: 16px;">
+                    <div style="display: flex; justify-content: space-between; margin-bottom: 4px;">
+                        <strong>{e} {l}</strong> 
+                        <span style="font-family:monospace;">{pct:.1f}%</span>
+                    </div>
+                    <div style="background-color: var(--background-fill-secondary); border: 1px solid var(--border-color-primary); border-radius: 6px; width: 100%; height: 10px;">
+                        <div style="background-color: #8b5cf6; width: {pct}%; height: 100%; border-radius: 5px;"></div>
+                    </div>
+                </div>
+                """
+                result_html += bar_html
+
+            # Format data for the full distribution chart
+            chart_data = {
+                self._format_label(id2label[i]): float(probs[i].item())
+                for i in range(len(probs))
+            }
+            
+            return result_html, chart_data
+
+        except Exception as e:
+            logger.error(f"Inference error: {e}")
+            return f"<div style='color: #ef4444;'>❌ <b>System Error:</b> Inference failed. Check application logs.</div>", None
+
+# Initialize application backend
+app_backend = MagicSupportClassifier()
+
+# High-value test scenarios based on Bitext taxonomy
+EXAMPLES = [
+    ["I need to cancel my order immediately, it was placed by mistake.", 5],
+    ["Where can I find the invoice for my last purchase?", 3],
+    ["The item arrived damaged and I want a full refund.", 5],
+    ["How do I change the shipping address on my account?", 3],
+    ["I forgot my password and cannot log in.", 3],
+    ["Are there any hidden fees if I cancel my subscription now?", 5],
+]
+
+# Build Gradio Interface
+with gr.Blocks(
+    theme=gr.themes.Soft(primary_hue="violet", secondary_hue="slate"),
+    title="MagicSupport Intent Classifier R&D Dashboard",
+    css="""
+        .header-box { text-align: center; padding: 25px; background: var(--background-fill-secondary); border-radius: 10px; border: 1px solid var(--border-color-primary); margin-bottom: 20px;}
+        .header-box h1 { color: var(--body-text-color); margin-bottom: 5px; }
+        .header-box p { color: var(--body-text-color-subdued); font-size: 16px; margin-top: 0; }
+        .badge { display: inline-block; padding: 4px 12px; border-radius: 12px; font-size: 13px; font-weight: 600; margin: 4px; }
+        .domain-badge { background: #ede9fe; color: #5b21b6; border: 1px solid #ddd6fe;}
+        .metric-badge { background: #f1f5f9; color: #334155; border: 1px solid #cbd5e1;}
+        footer { display: none !important; }
+    """
+) as demo:
+
+    gr.HTML("""
+    <div class="header-box">
+        <h1>🎧 MagicSupport Intent Classifier</h1>
+        <p>
+            High-precision semantic routing for automated customer support pipelines.
+        </p>
+        <div style="margin-top:12px;">
+            <span class="badge domain-badge">E-commerce & Retail</span>
+            <span class="badge domain-badge">Account Management</span>
+            <span class="badge domain-badge">Billing & Refunds</span>
+            <span class="badge metric-badge">Based on Bitext Taxonomy</span>
+        </div>
+    </div>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=5):
+            text_input = gr.Textbox(
+                label="Input Customer Query",
+                placeholder="Type a customer message here (e.g., 'Where is my package?')...",
+                lines=3,
+            )
+            
+            with gr.Row():
+                top_k_slider = gr.Slider(
+                    minimum=1, maximum=15, value=5, step=1,
+                    label="Display Top-K Predictions"
+                )
+            
+            with gr.Row():
+                predict_btn = gr.Button("🔍 Execute Prediction", variant="primary")
+                clear_btn = gr.Button("🗑️ Clear Interface", variant="secondary")
+                
+            gr.Examples(
+                examples=EXAMPLES,
+                inputs=[text_input, top_k_slider],
+                label="Actionable Test Scenarios",
+                examples_per_page=6,
+            )
+
+        with gr.Column(scale=5):
+            result_output = gr.HTML(label="Inference Results")
+
+    with gr.Row():
+        chart_output = gr.Label(
+            label="Full Semantic Distribution Map",
+            num_top_classes=app_backend.num_classes  # Dynamically set based on model config
+        )
+
+    with gr.Accordion("⚙️ Technical Architecture & Model Details", open=False):
+        gr.Markdown("""
+        ### Core Specifications
+        * **Target Model:** `learn-abc/magicSupport-intent-classifier`
+        * **Objective:** Multi-class text sequence classification for customer support routing.
+        * **Dataset Lineage:** Trained on the comprehensive `bitext/Bitext-customer-support-llm-chatbot-training-dataset`.
+        
+        ### Pipeline Features
+        * **Dynamic Label Resolution:** The UI heuristic engine automatically maps raw dataset labels (e.g., `change_shipping_address`) into clean, professional UI elements (e.g., Change Shipping Address) and assigns contextual iconography.
+        * **Optimized Inference:** Utilizes PyTorch `inference_mode` for reduced memory footprint and accelerated compute during forward passes.
+        """)
+
+    # Event Wiring
+    predict_btn.click(
+        fn=app_backend.predict,
+        inputs=[text_input, top_k_slider],
+        outputs=[result_output, chart_output],
+    )
+    text_input.submit(
+        fn=app_backend.predict,
+        inputs=[text_input, top_k_slider],
+        outputs=[result_output, chart_output],
+    )
+    clear_btn.click(
+        fn=lambda: ("", 5, "", None),
+        outputs=[text_input, top_k_slider, result_output, chart_output],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+torch
+transformers