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 BankingIntentClassifier: """ Encapsulates the intent classification model for scalable deployment. """ def __init__(self, model_id: str = "learn-abc/banking-multilingual-intent-classifier"): self.model_id = model_id self.max_length = 64 self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.intent_info = { "ACCOUNT_INFO": ("🏦", "Account Information"), "ATM_SUPPORT": ("🏧", "ATM Support"), "CARD_ISSUE": ("💳", "Card Issue"), "CARD_MANAGEMENT": ("⚙️", "Card Management"), "CARD_REPLACEMENT": ("🔄", "Card Replacement"), "CHECK_BALANCE": ("💰", "Check Balance"), "EDIT_PERSONAL_DETAILS": ("✏️", "Edit Personal Details"), "FAILED_TRANSFER": ("❌", "Failed Transfer"), "FALLBACK": ("🤔", "Out of Scope / Fallback"), "FEES": ("📋", "Fees & Charges"), "GREETING": ("👋", "Greeting"), "LOST_OR_STOLEN_CARD": ("🚨", "Lost or Stolen Card"), "MINI_STATEMENT": ("📄", "Mini Statement"), "TRANSFER": ("💸", "Transfer"), } 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) # Critical Memory Optimization: Force float16 to prevent Free Tier OOM kills self.model = AutoModelForSequenceClassification.from_pretrained( self.model_id, torch_dtype=torch.float16 ) self.model.to(self.device) self.model.eval() logger.info("Model loaded successfully.") except Exception as e: logger.error(f"Failed to load model: {e}") raise @torch.inference_mode() def predict(self, text: str, top_k: int = 3): if not text or not text.strip(): return "
⚠️ Input Required: Please enter a banking query.
", 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() # Ensure proper tensor dimensions for single-batch inputs if probs.dim() == 0: probs = probs.unsqueeze(0) top_indices = torch.topk(probs, k=top_k).indices.tolist() top_probs = torch.topk(probs, k=top_k).values.tolist() id2label = self.model.config.id2label # Primary Prediction Formatting using Pure HTML top_intent = id2label[top_indices[0]] emoji, label = self.intent_info.get(top_intent, ("🔹", top_intent)) confidence = top_probs[0] * 100 result_html = f"""

{emoji} {label}

Confidence: {confidence:.1f}%

📊 Top {top_k} Predictions

""" # HTML-based progress bars for visual clarity for idx, prob in zip(top_indices, top_probs): intent = id2label[idx] e, l = self.intent_info.get(intent, ("🔹", intent)) pct = prob * 100 bar_html = f"""
{e} {l} {pct:.1f}%
""" result_html += bar_html # Format data for Gradio Label component chart_data = { 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"
❌ Error: An internal error occurred during processing. Check logs.
", None # Initialize application backend app_backend = BankingIntentClassifier() # Define UI Dataset Examples EXAMPLES = [ ["can you check how much money I have in my account?", "English"], ["আমার ব্যালেন্স কত?", "Bangla"], ["amar card hariye geche, block koro", "Banglish"], ["last 10 transaction dekhao", "Code-mixed"], ["ATM থেকে টাকা বের হচ্ছে না", "Bangla"], ["my transfer failed, what happened?", "English"], ["fee keno kete nilo without reason", "Code-mixed"], ["hello, I need some help", "English"], ["who won the cricket match yesterday?", "Fallback"], ["card ta replace korte chai", "Banglish"], ["আমার পিন পরিবর্তন করতে চাই", "Bangla"], ["I lost my debit card, please block it immediately", "English"], ] # Build Gradio Interface with gr.Blocks( theme=gr.themes.Soft(primary_hue="blue", secondary_hue="slate"), title="Banking 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; } .lang-badge { background: #e0f2fe; color: #0369a1; border: 1px solid #bae6fd;} .metric-badge { background: #dcfce7; color: #15803d; border: 1px solid #bbf7d0;} footer { display: none !important; } """ ) as demo: gr.HTML("""

🏦 Multilingual Banking Intent Classifier

Advanced fine-tuned classification for 14 production banking intents.

🇬🇧 English 🇧🇩 Bangla ✍️ Banglish 🔀 Code-mixed ✅ Acc: ~98.4% 📊 F1: ~0.982
""") with gr.Row(): with gr.Column(scale=5): text_input = gr.Textbox( label="Input Banking Query", placeholder="Type a query here (e.g., 'check my balance' or 'amar card block koro')...", lines=3, ) with gr.Row(): top_k_slider = gr.Slider( minimum=1, maximum=14, value=3, 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=[[e[0]] for e in EXAMPLES], inputs=text_input, label="Test Scenarios", examples_per_page=4, ) with gr.Column(scale=5): result_output = gr.HTML(label="Inference Results") # Chart output moved to its own full-width row with gr.Row(): chart_output = gr.Label( label="Full Distribution Map", num_top_classes=14 ) with gr.Accordion("⚙️ Technical Architecture & Model Details", open=False): gr.Markdown(""" ### Core Specifications * **Base Architecture:** `google/muril-base-cased` (Optimized for Indian vernaculars). * **Objective:** Multi-class text sequence classification. * **Supported Lexicons:** English, Standard Bengali, Romanized Bengali (Banglish), Code-mixed syntax. ### Intent Taxonomy | ID | Description | |---|---| | ACCOUNT_INFO | General account status and balance queries | | ATM_SUPPORT | Hardware issues and withdrawal failures | | CARD_ISSUE | Malfunctions and terminal declines | | CARD_MANAGEMENT | PIN resets, activation, and security controls | | CARD_REPLACEMENT | Physical card replacement requests | | CHECK_BALANCE | Direct balance inquiries | | EDIT_PERSONAL_DETAILS | KYC and profile updates | | FAILED_TRANSFER | Routing and transaction failures | | FALLBACK | Non-banking or ambiguous queries | | FEES | Service charge inquiries | | GREETING | Conversational anchors | | LOST_OR_STOLEN_CARD | Emergency block requests | | MINI_STATEMENT | Ledger and transaction history | | TRANSFER | Fund routing instructions | ### Data Lineage Derived from the `learn-abc/banking14-intents-en-bn-banglish` dataset. The original 77 intents were aggressively condensed into 14 high-value production categories. The corpus was subsequently augmented with synthetically generated multilingual data translated via advanced neural machine translation models. """) # 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: ("", "", None), outputs=[text_input, result_output, chart_output], ) if __name__ == "__main__": demo.launch()