Update frontend_ui.py

frontend_ui.py

@@ -1512,119 +1512,3 @@ 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()