Update frontend_ui.py

ui file updated with mcp

frontend_ui.py

@@ -1512,3 +1512,119 @@ def get_batch_processing_status():
             
     except Exception as e:
         return f"❌ Error getting status: {str(e)}", "", {}
+
+import gradio as gr
+import pandas as pd
+import time
+import threading
+import asyncio
+import sys
+import os
+import datetime
+from src.heavy_workload_demo import ModalContainerScalingDemo, RealTimeBatchProcessor
+
+# Import dashboard functions from app.py to ensure proper integration
+sys.path.append(os.path.dirname(__file__))
+# Use dynamic import to avoid circular dependency issues
+dashboard_state = None
+add_file_to_dashboard = None
+get_dashboard_status = None
+get_processing_queue = None
+get_dashboard_metrics = None
+get_jobs_history = None
+
+def _ensure_app_imports():
+    """Dynamically import app functions to avoid circular dependencies"""
+    global dashboard_state, add_file_to_dashboard, get_dashboard_status
+    global get_processing_queue, get_dashboard_metrics, get_jobs_history
+    
+    if dashboard_state is None:
+        try:
+            from app import (
+                dashboard_state as _dashboard_state,
+                add_file_to_dashboard as _add_file_to_dashboard,
+                get_dashboard_status as _get_dashboard_status,
+                get_processing_queue as _get_processing_queue,
+                get_dashboard_metrics as _get_dashboard_metrics,
+                get_jobs_history as _get_jobs_history
+            )
+            dashboard_state = _dashboard_state
+            add_file_to_dashboard = _add_file_to_dashboard
+            get_dashboard_status = _get_dashboard_status
+            get_processing_queue = _get_processing_queue
+            get_dashboard_metrics = _get_dashboard_metrics
+            get_jobs_history = _get_jobs_history
+        except ImportError as e:
+            print(f"Warning: Could not import dashboard functions: {e}")
+            # Set fallback functions that return empty data
+            dashboard_state = {"active_tasks": 0, "total_files": 0}
+            add_file_to_dashboard = lambda *args, **kwargs: None
+            get_dashboard_status = lambda: "📊 Dashboard not available"
+            get_processing_queue = lambda: [["Status", "Not Available"]]
+            get_dashboard_metrics = lambda: [["Metric", "Not Available"]]
+            get_jobs_history = lambda: []
+
+# Initialize demo components
+heavy_workload_demo = ModalContainerScalingDemo()
+batch_processor = RealTimeBatchProcessor()
+
+# Global reference to dashboard function (set by create_medical_ui)
+_add_file_to_dashboard = None
+
+def is_modal_available():
+    """Check if Modal environment is available"""
+    try:
+        import modal
+        return True
+    except ImportError:
+        return False
+
+def get_environment_name():
+    """Get current deployment environment name"""
+    if is_modal_available():
+        return "Modal Cloud"
+    else:
+        return "Local/HuggingFace"
+
+def create_text_processing_tab(process_text_only, cancel_current_task, get_dashboard_status,
+                              dashboard_state, get_dashboard_metrics):
+    """Create the text processing tab"""
+    
+    with gr.Tab("📝 Text Processing"):
+        gr.Markdown("### Medical Text Analysis")
+        gr.Markdown("Process medical text directly with entity extraction and FHIR generation")
+        
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### Medical Text Input")
+                text_input = gr.Textbox(
+                    label="Medical Text",
+                    placeholder="Enter medical text here...",
+                    lines=8
+                )
+                
+                enable_fhir_text = gr.Checkbox(
+                    label="Generate FHIR Resources",
+                    value=False
+                )
+                
+                with gr.Row():
+                    process_text_btn = gr.Button("🔍 Process Text", variant="primary")
+                    cancel_text_btn = gr.Button("❌ Cancel", variant="secondary", visible=False)
+            
+            with gr.Column():
+                # Additional UI components here
+                pass
+
+# Add the Gradio app launch call with mcp_server=True
+def launch_gradio_app():
+    demo = gr.Interface(
+        fn=lambda x: x,
+        inputs=gr.Textbox(label="Input"),
+        outputs=gr.Textbox(label="Output"),
+        title="FhirFlame Demo"
+    )
+    demo.launch(mcp_server=True)
+
+if __name__ == "__main__":
+    launch_gradio_app()