Upload demo_app.py with huggingface_hub

demo_app.py

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+"""
+Web Demo — Bengali Public Service Dialog System
+=================================================
+Simple Gradio-based chat interface for demonstrating the dialog system.
+Can be run on Colab or locally.
+
+Usage (Colab):
+    !pip install -q gradio
+    !python demo_app.py \
+        --nlu_model_dir /content/models/joint_intent_ner \
+        --gen_model_dir /content/models/response_gen/best_model \
+        --labels_dir /content/data/processed/labels
+
+Usage (local):
+    python demo_app.py --nlu_model_dir ... --gen_model_dir ... --labels_dir ...
+"""
+
+import argparse
+import json
+import os
+import sys
+import uuid
+
+import torch
+
+# Ensure project modules are importable
+script_dir = os.path.dirname(os.path.abspath(__file__))
+project_dir = os.path.dirname(script_dir)
+sys.path.insert(0, project_dir)
+sys.path.insert(0, os.path.join(project_dir, "model"))
+sys.path.insert(0, script_dir)
+
+from pipeline import DialogPipeline
+
+
+def create_demo(pipeline: DialogPipeline):
+    """Build the Gradio chat interface."""
+    import gradio as gr
+
+    # Track conversations per session
+    conversations = {}
+
+    def chat(message, history, domain):
+        # Get or create conversation ID
+        conv_id = "demo_" + str(id(history)) if history else "demo_" + uuid.uuid4().hex[:8]
+
+        if not history:
+            pipeline.start_conversation(conv_id, domain)
+
+        result = pipeline.respond(conv_id, message, domain_hint=domain)
+
+        # Build info string
+        info = (
+            f"Intent: {result['intent']} ({result['confidence']:.2f}) | "
+            f"Domain: {result['domain']} | "
+            f"State: {result['state']}"
+        )
+        if result["entities"]:
+            info += f" | Entities: {result['entities']}"
+        if result["should_escalate"]:
+            info += " | ⚠️ ESCALATION SUGGESTED"
+
+        response = result["response"]
+        if result["filled_slots"]:
+            response += f"\n\n---\n🔍 {info}"
+        else:
+            response += f"\n\n---\n🔍 {info}"
+
+        return response
+
+    demo = gr.ChatInterface(
+        fn=chat,
+        additional_inputs=[
+            gr.Dropdown(
+                choices=["general", "passport", "nid", "utilities", "welfare"],
+                value="general",
+                label="Service Domain / সেবার ধরন",
+            ),
+        ],
+        title="🇧🇩 Bengali Public Service Dialog System",
+        description=(
+            "বাংলাদেশ সরকারি সেবা সহায়তা ব্যবস্থা\n\n"
+            "Ask questions about passport, NID, utilities, welfare services "
+            "in Bengali (Standard, Sylheti, or Chittagonian dialect)."
+        ),
+        examples=[
+            ["আমি পাসপোর্ট করতে চাই।", "passport"],
+            ["বিদ্যুৎ বিল কীভাবে দেব?", "utilities"],
+            ["বয়স্ক ভাতার জন্য আবেদন করতে চাই।", "welfare"],
+            ["হামি এনআইডি কার্ড বানাইতে চাই।", "nid"],
+            ["আঁই পাসপোর্ট বানাইত্তে সাই।", "passport"],
+        ],
+        theme=gr.themes.Soft(),
+    )
+
+    return demo
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--nlu_model_dir", type=str, required=True)
+    parser.add_argument("--gen_model_dir", type=str, required=True)
+    parser.add_argument("--labels_dir", type=str, required=True)
+    parser.add_argument("--bert_model", type=str,
+                        default="sagorsarker/bangla-bert-base")
+    parser.add_argument("--port", type=int, default=7860)
+    parser.add_argument("--share", action="store_true",
+                        help="Create public Gradio link")
+    args = parser.parse_args()
+
+    # Load pipeline
+    pipeline = DialogPipeline(
+        nlu_model_path=args.nlu_model_dir,
+        gen_model_path=args.gen_model_dir,
+        labels_dir=args.labels_dir,
+        bert_model=args.bert_model,
+    )
+
+    # Create and launch demo
+    demo = create_demo(pipeline)
+    demo.launch(server_port=args.port, share=args.share)
+
+
+if __name__ == "__main__":
+    main()