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import gradio as gr
import time
import json
import os
import subprocess
from datetime import datetime, timedelta
from typing import List, Tuple, Optional
from backend import (
ALLOWED_LANGS, AUDIO_FORMATS, transcription_manager,
allowed_file, User
)
from ai_summary import ai_summary_manager
def format_status(status):
"""Convert status to user-friendly format"""
status_map = {
'pending': '⏳ Queued',
'processing': '🔄 Processing',
'completed': '✅ Done',
'failed': '❌ Failed'
}
return status_map.get(status, status)
def format_processing_time(created_at, completed_at=None):
"""Calculate and format processing time"""
try:
start_time = datetime.fromisoformat(created_at)
if completed_at:
end_time = datetime.fromisoformat(completed_at)
duration = end_time - start_time
else:
duration = datetime.now() - start_time
total_seconds = int(duration.total_seconds())
if total_seconds < 60:
return f"{total_seconds}s"
elif total_seconds < 3600:
minutes = total_seconds // 60
seconds = total_seconds % 60
return f"{minutes}m {seconds}s"
else:
hours = total_seconds // 3600
minutes = (total_seconds % 3600) // 60
return f"{hours}h {minutes}m"
except:
return "Unknown"
def get_user_stats_display(user: User):
"""Get comprehensive user statistics for display"""
if not user:
return "👤 Please log in to view statistics"
try:
# Get transcript stats
transcript_stats = transcription_manager.get_user_stats(user.user_id)
# Get AI summary stats
summary_stats = transcription_manager.get_user_summary_stats(user.user_id)
total_transcripts = transcript_stats.get('total_jobs', 0)
total_summaries = summary_stats.get('total_jobs', 0)
stats_text = f"👤 {user.username} | 🎙️ Transcripts: {total_transcripts} | 🤖 AI Summaries: {total_summaries}"
# Add processing status
processing_transcripts = transcript_stats.get('by_status', {}).get('processing', 0)
processing_summaries = summary_stats.get('by_status', {}).get('processing', 0)
if processing_transcripts > 0:
stats_text += f" | 🔄 Transcribing: {processing_transcripts}"
if processing_summaries > 0:
stats_text += f" | 🔄 Summarizing: {processing_summaries}"
return stats_text
except Exception as e:
return f"👤 {user.username} | Stats error: {str(e)}"
# Authentication Functions (same as before)
def register_user(email, username, password, confirm_password, gdpr_consent, data_retention_consent, marketing_consent):
"""Register new user account"""
try:
print(f"📝 Registration attempt for: {username} ({email})")
# Validate inputs
if not email or not username or not password:
return "❌ All fields are required", gr.update(visible=False)
if password != confirm_password:
return "❌ Passwords do not match", gr.update(visible=False)
if not gdpr_consent:
return "❌ GDPR consent is required to create an account", gr.update(visible=False)
if not data_retention_consent:
return "❌ Data retention agreement is required", gr.update(visible=False)
# Attempt registration
success, message, user_id = transcription_manager.register_user(
email, username, password, gdpr_consent, data_retention_consent, marketing_consent
)
print(f"📝 Registration result: success={success}, message={message}")
if success:
print(f"✅ User registered successfully: {username}")
return f"✅ {message}! Please log in with your credentials.", gr.update(visible=True)
else:
print(f"❌ Registration failed: {message}")
return f"❌ {message}", gr.update(visible=False)
except Exception as e:
print(f"❌ Registration error: {str(e)}")
return f"❌ Registration error: {str(e)}", gr.update(visible=False)
def login_user(login, password):
"""Login user"""
try:
print(f"🔐 Login attempt for: {login}")
if not login or not password:
return "❌ Please enter both username/email and password", None, gr.update(visible=True), gr.update(visible=False), "👤 Please log in to view your statistics..."
success, message, user = transcription_manager.login_user(login, password)
print(f"🔐 Login result: success={success}, message={message}")
if success and user:
print(f"✅ User logged in successfully: {user.username}")
stats_display = get_user_stats_display(user)
return f"✅ Welcome back, {user.username}!", user, gr.update(visible=False), gr.update(visible=True), stats_display
else:
print(f"❌ Login failed: {message}")
return f"❌ {message}", None, gr.update(visible=True), gr.update(visible=False), "👤 Please log in to view your statistics..."
except Exception as e:
print(f"❌ Login error: {str(e)}")
return f"❌ Login error: {str(e)}", None, gr.update(visible=True), gr.update(visible=False), "👤 Please log in to view your statistics..."
def logout_user():
"""Logout user"""
print("👋 User logged out")
return None, "👋 You have been logged out. Please log in to continue.", gr.update(visible=True), gr.update(visible=False), "👤 Please log in to view your statistics..."
# Transcription Functions
def submit_transcription(file, language, audio_format, diarization_enabled, speakers,
profanity, punctuation, timestamps, lexical, user):
"""Submit transcription job - requires authenticated user"""
if not user:
return (
"❌ Please log in to submit transcriptions",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
if file is None:
return (
"Please upload an audio or video file first.",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
try:
# Get file data
try:
if isinstance(file, str):
if os.path.exists(file):
with open(file, 'rb') as f:
file_bytes = f.read()
original_filename = os.path.basename(file)
else:
return (
"File not found. Please try uploading again.",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
else:
file_path = str(file)
if os.path.exists(file_path):
with open(file_path, 'rb') as f:
file_bytes = f.read()
original_filename = os.path.basename(file_path)
else:
return (
"Unable to process file. Please try again.",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
except Exception as e:
return (
f"Error reading file: {str(e)}",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
# Validate file
file_extension = original_filename.split('.')[-1].lower() if '.' in original_filename else ""
supported_extensions = set(AUDIO_FORMATS) | {
'mp4', 'mov', 'avi', 'mkv', 'webm', 'm4a', '3gp', 'f4v',
'wmv', 'asf', 'rm', 'rmvb', 'flv', 'mpg', 'mpeg', 'mts', 'vob'
}
if file_extension not in supported_extensions and file_extension != "":
return (
f"Unsupported file format: .{file_extension}",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
# Basic file size check
if len(file_bytes) > 500 * 1024 * 1024: # 500MB limit
return (
"File too large. Please upload files smaller than 500MB.",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
# Prepare settings
settings = {
'audio_format': audio_format,
'diarization_enabled': diarization_enabled,
'speakers': speakers,
'profanity': profanity,
'punctuation': punctuation,
'timestamps': timestamps,
'lexical': lexical
}
# Submit job
job_id = transcription_manager.submit_transcription(
file_bytes, original_filename, user.user_id, language, settings
)
# Update job state
job_state = {
'current_job_id': job_id,
'start_time': datetime.now().isoformat(),
'auto_refresh_active': True,
'last_status': 'pending'
}
# Get updated user stats
stats_display = get_user_stats_display(user)
return (
f"🚀 Transcription started for: {original_filename}\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"Job ID: {job_id}",
job_state,
gr.update(visible=True, value="🔄 Auto-refresh active"),
stats_display
)
except Exception as e:
print(f"❌ Error submitting transcription: {str(e)}")
return (
f"Error: {str(e)}",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
def check_current_job_status(job_state, user):
"""Check status of current job with improved transcript handling"""
if not user:
return (
"❌ Please log in to check status",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
if not job_state or 'current_job_id' not in job_state:
return (
"No active job",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
job_id = job_state['current_job_id']
try:
job = transcription_manager.get_job_status(job_id)
if not job or job.user_id != user.user_id:
return (
"Job not found or access denied",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
# Calculate processing time
processing_time = format_processing_time(job.created_at, job.completed_at)
# Enhanced status change logging
last_status = job_state.get('last_status', '')
if job.status != last_status:
print(f"🔄 [{user.username}] Job status change: {last_status} → {job.status} ({job.original_filename})")
job_state['last_status'] = job.status
# Get updated user stats
stats_display = get_user_stats_display(user)
# Handle completed status with better transcript detection
if job.status == 'completed' and job.transcript_text and job.transcript_text.strip():
# Job is complete and transcript is available, stop auto-refresh
job_state['auto_refresh_active'] = False
# Create downloadable file
try:
transcript_file = create_transcript_file(job.transcript_text, job_id)
print(f"✅ [{user.username}] Transcription ready: {len(job.transcript_text)} characters")
except Exception as e:
print(f"⚠️ [{user.username}] Error creating transcript file: {str(e)}")
transcript_file = None
return (
f"✅ Transcription completed in {processing_time}",
job.transcript_text,
gr.update(visible=True, value=transcript_file) if transcript_file else gr.update(visible=False),
f"Processed: {job.original_filename}",
gr.update(visible=False), # Hide auto-refresh status
stats_display
)
elif job.status == 'failed':
# Job failed, stop auto-refresh
job_state['auto_refresh_active'] = False
error_msg = job.error_message[:100] + "..." if job.error_message and len(job.error_message) > 100 else job.error_message or "Unknown error"
return (
f"❌ Transcription failed after {processing_time}",
"",
gr.update(visible=False),
f"Error: {error_msg}",
gr.update(visible=False),
stats_display
)
elif job.status == 'processing':
# Still processing, continue auto-refresh
auto_refresh_active = job_state.get('auto_refresh_active', False)
return (
f"🔄 Processing... ({processing_time} elapsed)\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"Converting and analyzing: {job.original_filename}",
gr.update(visible=True, value="🔄 Auto-refresh active") if auto_refresh_active else gr.update(visible=False),
stats_display
)
elif job.status == 'completed' and (not job.transcript_text or not job.transcript_text.strip()):
# Job marked as completed but transcript not yet available - keep refreshing
auto_refresh_active = job_state.get('auto_refresh_active', False)
return (
f"🔄 Finalizing transcript... ({processing_time} elapsed)\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"Retrieving results: {job.original_filename}",
gr.update(visible=True, value="🔄 Auto-refresh active") if auto_refresh_active else gr.update(visible=False),
stats_display
)
else: # pending
# Still pending, continue auto-refresh
auto_refresh_active = job_state.get('auto_refresh_active', False)
return (
f"⏳ Queued for processing... ({processing_time} waiting)\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"Waiting: {job.original_filename}",
gr.update(visible=True, value="🔄 Auto-refresh active") if auto_refresh_active else gr.update(visible=False),
stats_display
)
except Exception as e:
print(f"❌ Error checking job status: {str(e)}")
return (
f"Error checking status: {str(e)}",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
# AI Summary Functions
def get_available_transcripts(user):
"""Get list of available transcripts for AI summarization"""
if not user:
return gr.update(choices=[], value=[])
try:
# Get completed transcripts
completed_jobs = transcription_manager.get_user_history(user.user_id, limit=50)
completed_transcripts = [
job for job in completed_jobs
if job.status == 'completed' and job.transcript_text
]
# Create choices list
choices = []
for job in completed_transcripts[:20]: # Limit to recent 20
label = f"{job.original_filename} ({job.created_at[:16]})"
choices.append((label, job.job_id))
return gr.update(choices=choices, value=[])
except Exception as e:
print(f"❌ Error getting available transcripts: {str(e)}")
return gr.update(choices=[], value=[])
def submit_ai_summary_enhanced(existing_transcripts, new_audio_video_file, document_image_files,
ai_instructions, summary_format, output_language, focus_areas,
include_timestamps, include_action_items, user):
"""Enhanced AI summary submission with immediate transcript processing"""
if not user:
return (
"❌ Please log in to generate AI summaries",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
# Determine content type and validate inputs
has_existing_transcripts = existing_transcripts and len(existing_transcripts) > 0
has_new_audio_video = new_audio_video_file is not None
has_document_images = document_image_files and len(document_image_files) > 0
if not has_existing_transcripts and not has_new_audio_video and not has_document_images:
return (
"❌ Please provide content: select existing transcripts, upload audio/video file, or upload documents/images",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
if not ai_instructions.strip():
return (
"❌ Please provide AI instructions for the summary",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
try:
# Handle new audio/video file - submit for transcription but don't create AI job yet
transcription_job_id = None
if has_new_audio_video:
# Submit audio/video file to transcription service first
try:
# Read the uploaded file
if isinstance(new_audio_video_file, str):
file_path = new_audio_video_file
else:
file_path = str(new_audio_video_file)
with open(file_path, 'rb') as f:
file_bytes = f.read()
original_filename = os.path.basename(file_path)
# Use default transcription settings optimized for AI Summary
transcription_settings = {
'audio_format': 'wav',
'diarization_enabled': True,
'speakers': 5, # Allow more speakers for conferences
'profanity': 'masked',
'punctuation': 'automatic',
'timestamps': True,
'lexical': False
}
# Submit to transcription service with Thai as default language
transcription_job_id = transcription_manager.submit_transcription(
file_bytes,
original_filename,
user.user_id,
"th-TH", # Default to Thai
transcription_settings
)
print(f"🎙️ [{user.username}] Audio/video submitted for transcription: {transcription_job_id[:8]}...")
# Create a special job state that will trigger AI summary when transcription completes
summary_job_state = {
'waiting_for_transcription': True,
'transcription_job_id': transcription_job_id,
'start_time': datetime.now().isoformat(),
'auto_refresh_active': True,
'last_status': 'waiting_for_transcription',
'ai_instructions': ai_instructions,
'summary_format': summary_format,
'output_language': output_language,
'focus_areas': focus_areas,
'include_timestamps': include_timestamps,
'include_action_items': include_action_items,
'existing_transcripts': existing_transcripts if existing_transcripts else [],
'document_image_files': document_image_files if document_image_files else [],
'user_id': user.user_id
}
# Get updated user stats
stats_display = get_user_stats_display(user)
return (
f"🎙️ Audio/video submitted for transcription\n⏳ AI Summary will start automatically when transcription completes\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"Transcription Job: {transcription_job_id[:8]}... → Will auto-trigger AI Summary",
summary_job_state,
gr.update(visible=True, value="🔄 Waiting for transcription"),
stats_display
)
except Exception as e:
print(f"❌ Error submitting audio/video for transcription: {str(e)}")
return (
f"❌ Error processing audio/video file: {str(e)}",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
# For existing transcripts or documents only (no audio/video transcription needed)
else:
transcript_ids = existing_transcripts if existing_transcripts else []
document_files = document_image_files if document_image_files else []
# Determine content mode
if has_existing_transcripts and not has_document_images:
content_mode = "Existing Transcripts"
elif has_document_images and not has_existing_transcripts:
content_mode = "Text Documents"
else:
content_mode = "Mixed Content"
# Prepare settings
settings = {
'content_mode': content_mode,
'format': summary_format,
'output_language': output_language,
'focus_areas': focus_areas,
'include_timestamps': include_timestamps,
'include_action_items': include_action_items,
'language': "th-TH"
}
# Submit AI summary job immediately (no transcription needed)
job_id = ai_summary_manager.submit_summary_job_enhanced(
user_id=user.user_id,
content_mode=content_mode,
summary_type=summary_format,
user_prompt=ai_instructions,
existing_transcript_ids=transcript_ids,
audio_video_files=[],
document_files=document_files,
settings=settings
)
# Update job state
summary_job_state = {
'current_summary_job_id': job_id,
'start_time': datetime.now().isoformat(),
'auto_refresh_active': True,
'last_status': 'pending'
}
# Get updated user stats
stats_display = get_user_stats_display(user)
# Create source description
source_parts = []
if has_existing_transcripts:
source_parts.append(f"{len(transcript_ids)} existing transcripts")
if has_document_images:
source_parts.append(f"{len(document_files)} document/image files")
source_info = " + ".join(source_parts)
return (
f"🤖 AI Summary started with {source_info}\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"AI Job ID: {job_id}",
summary_job_state,
gr.update(visible=True, value="🔄 AI Auto-refresh active"),
stats_display
)
except Exception as e:
print(f"❌ Error submitting enhanced AI summary: {str(e)}")
return (
f"❌ Error: {str(e)}",
"",
gr.update(visible=False),
"",
{},
gr.update(visible=False),
gr.update()
)
def check_ai_summary_status(summary_job_state, user):
"""Check status of AI summary job with auto-trigger logic for completed transcriptions"""
if not user:
return (
"❌ Please log in to check AI summary status",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
if not summary_job_state:
return (
"No active AI summary job",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
try:
# Handle special case: waiting for transcription to complete
if summary_job_state.get('waiting_for_transcription'):
transcription_job_id = summary_job_state.get('transcription_job_id')
if not transcription_job_id:
return (
"❌ Error: Missing transcription job ID",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
# Check transcription status
transcription_job = transcription_manager.get_job_status(transcription_job_id)
if not transcription_job:
return (
"❌ Transcription job not found",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
processing_time = format_processing_time(summary_job_state['start_time'])
if transcription_job.status == 'pending':
return (
f"⏳ Transcription queued... ({processing_time} elapsed)\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"Transcription: {transcription_job.original_filename}",
gr.update(visible=True, value="🔄 Waiting for transcription"),
get_user_stats_display(user)
)
elif transcription_job.status == 'processing':
transcription_time = format_processing_time(transcription_job.created_at)
return (
f"🎙️ Transcribing... ({transcription_time} transcribing, {processing_time} total)\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"Transcribing: {transcription_job.original_filename}",
gr.update(visible=True, value="🔄 Transcription in progress"),
get_user_stats_display(user)
)
elif transcription_job.status == 'failed':
summary_job_state['auto_refresh_active'] = False
return (
f"❌ Transcription failed - Cannot proceed\nError: {transcription_job.error_message or 'Unknown error'}",
"",
gr.update(visible=False),
f"Failed: {transcription_job.original_filename}",
gr.update(visible=False),
get_user_stats_display(user)
)
elif transcription_job.status == 'completed':
if not transcription_job.transcript_text or not transcription_job.transcript_text.strip():
return (
f"🔄 Transcription completed, retrieving text... ({processing_time} elapsed)\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"Getting transcript: {transcription_job.original_filename}",
gr.update(visible=True, value="🔄 Getting transcript"),
get_user_stats_display(user)
)
# TRANSCRIPTION IS COMPLETE! NOW TRIGGER AI SUMMARY IMMEDIATELY
print(f"✅ Transcription completed, triggering AI summary immediately...")
try:
# Prepare transcript IDs including the newly completed one
transcript_ids = summary_job_state.get('existing_transcripts', [])
transcript_ids.append(transcription_job_id)
# Prepare settings
settings = {
'content_mode': "New Audio/Video Files",
'format': summary_job_state.get('summary_format', 'บทสรุปผู้บริหาร'),
'output_language': summary_job_state.get('output_language', 'Thai'),
'focus_areas': summary_job_state.get('focus_areas', ''),
'include_timestamps': summary_job_state.get('include_timestamps', True),
'include_action_items': summary_job_state.get('include_action_items', True),
'language': "th-TH"
}
# Submit AI summary job NOW with completed transcript
job_id = ai_summary_manager.submit_summary_job_enhanced(
user_id=summary_job_state['user_id'],
content_mode="New Audio/Video Files",
summary_type=summary_job_state.get('summary_format', 'บทสรุปผู้บริหาร'),
user_prompt=summary_job_state.get('ai_instructions', ''),
existing_transcript_ids=transcript_ids,
audio_video_files=[],
document_files=summary_job_state.get('document_image_files', []),
settings=settings
)
print(f"🤖 AI Summary job created immediately: {job_id}")
# Update job state to track AI summary instead of transcription
summary_job_state.update({
'waiting_for_transcription': False,
'current_summary_job_id': job_id,
'transcription_completed_at': datetime.now().isoformat(),
'last_status': 'ai_started'
})
return (
f"✅ Transcription done! 🤖 AI Summary started immediately\n📊 Using transcript: {len(transcription_job.transcript_text):,} characters\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"AI Processing: {transcription_job.original_filename}",
gr.update(visible=True, value="🔄 AI Summary active"),
get_user_stats_display(user)
)
except Exception as e:
print(f"❌ Error triggering AI summary: {str(e)}")
return (
f"❌ Transcription completed but AI summary failed to start: {str(e)}",
"",
gr.update(visible=False),
"AI Summary creation failed",
gr.update(visible=False),
get_user_stats_display(user)
)
# Normal AI summary job monitoring
if 'current_summary_job_id' not in summary_job_state:
return (
"No active AI summary job",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
job_id = summary_job_state['current_summary_job_id']
job = ai_summary_manager.get_summary_status(job_id)
if not job or job.user_id != user.user_id:
return (
"AI summary job not found or access denied",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
# Calculate processing time
processing_time = format_processing_time(job.created_at, job.completed_at)
# Enhanced status change logging
last_status = summary_job_state.get('last_status', '')
if job.status != last_status:
print(f"🔄 [{user.username}] AI Summary status: {last_status} → {job.status}")
summary_job_state['last_status'] = job.status
# Get updated user stats
stats_display = get_user_stats_display(user)
# Handle completed status
if job.status == 'completed' and job.summary_text and job.summary_text.strip():
# Job is complete, stop auto-refresh
summary_job_state['auto_refresh_active'] = False
# Create downloadable file
try:
summary_file = create_summary_file(job.summary_text, job_id)
print(f"✅ [{user.username}] AI Summary ready: {len(job.summary_text)} characters")
except Exception as e:
print(f"⚠️ [{user.username}] Error creating summary file: {str(e)}")
summary_file = None
total_time = format_processing_time(summary_job_state['start_time'])
return (
f"✅ AI Summary completed! Total time: {total_time}\n📊 Generated: {len(job.summary_text):,} characters",
job.summary_text,
gr.update(visible=True, value=summary_file) if summary_file else gr.update(visible=False),
f"Completed: {', '.join(job.original_files)}",
gr.update(visible=False),
stats_display
)
elif job.status == 'failed':
# Job failed, stop auto-refresh
summary_job_state['auto_refresh_active'] = False
error_msg = job.error_message[:100] + "..." if job.error_message else "Unknown error"
total_time = format_processing_time(summary_job_state['start_time'])
return (
f"❌ AI Summary failed after {total_time}",
"",
gr.update(visible=False),
f"Error: {error_msg}",
gr.update(visible=False),
stats_display
)
elif job.status == 'processing':
# Still processing, continue auto-refresh
return (
f"🤖 AI analyzing and generating summary... ({processing_time} AI processing)\n📊 Creating comprehensive analysis\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"AI Processing: {', '.join(job.original_files[:2])}{'...' if len(job.original_files) > 2 else ''}",
gr.update(visible=True, value="🔄 AI generating summary"),
stats_display
)
else: # pending
# Still pending, continue auto-refresh
return (
f"⏳ AI Summary queued... ({processing_time} waiting)\n📡 Auto-refreshing every 10 seconds...",
"",
gr.update(visible=False),
f"Queued: {', '.join(job.original_files[:2])}{'...' if len(job.original_files) > 2 else ''}",
gr.update(visible=True, value="🔄 AI queued"),
stats_display
)
except Exception as e:
print(f"❌ Error checking AI summary status: {str(e)}")
return (
f"Error checking AI summary status: {str(e)}",
"",
gr.update(visible=False),
"",
gr.update(visible=False),
gr.update()
)
def should_auto_refresh(job_state, user):
"""Check if auto-refresh should be active"""
if not user or not job_state or not job_state.get('auto_refresh_active', False):
return False
if 'current_job_id' not in job_state:
return False
job_id = job_state['current_job_id']
try:
job = transcription_manager.get_job_status(job_id)
if not job or job.user_id != user.user_id:
return False
if job.status == 'failed':
return False
elif job.status == 'completed':
if job.transcript_text and job.transcript_text.strip():
return False
else:
return True
else:
return True
except Exception as e:
print(f"❌ Error in should_auto_refresh: {str(e)}")
return True
def should_auto_refresh_summary(summary_job_state, user):
"""Check if AI summary auto-refresh should be active"""
if not user or not summary_job_state or not summary_job_state.get('auto_refresh_active', False):
return False
if 'current_summary_job_id' not in summary_job_state:
return False
job_id = summary_job_state['current_summary_job_id']
try:
job = ai_summary_manager.get_summary_status(job_id)
if not job or job.user_id != user.user_id:
return False
if job.status in ['failed', 'completed']:
return False
else:
return True
except Exception as e:
print(f"❌ Error in should_auto_refresh_summary: {str(e)}")
return True
def auto_refresh_status(job_state, user):
"""Auto-refresh function for transcription"""
if not user:
return (
gr.update(),
gr.update(),
gr.update(),
gr.update(),
gr.update(visible=False),
gr.update()
)
if should_auto_refresh(job_state, user):
return check_current_job_status(job_state, user)
else:
return (
gr.update(),
gr.update(),
gr.update(),
gr.update(),
gr.update(visible=False),
gr.update()
)
def auto_refresh_ai_summary(summary_job_state, user):
"""Auto-refresh function for AI summary"""
if not user:
return (
gr.update(),
gr.update(),
gr.update(),
gr.update(),
gr.update(visible=False),
gr.update()
)
if should_auto_refresh_summary(summary_job_state, user):
return check_ai_summary_status(summary_job_state, user)
else:
return (
gr.update(),
gr.update(),
gr.update(),
gr.update(),
gr.update(visible=False),
gr.update()
)
# History Functions
def get_transcription_history_table(user, show_all=False):
"""Get transcription history table"""
if not user:
return []
try:
limit = 100 if show_all else 20
transcript_jobs = transcription_manager.get_user_history(user.user_id, limit=limit)
table_data = []
for job in transcript_jobs:
try:
created_time = datetime.fromisoformat(job.created_at)
formatted_date = created_time.strftime("%Y-%m-%d %H:%M")
except:
formatted_date = job.created_at[:16]
status_display = format_status(job.status)
time_display = format_processing_time(job.created_at, job.completed_at)
job_id_display = job.job_id[:8] + "..." if len(job.job_id) > 8 else job.job_id
language_display = ALLOWED_LANGS.get(job.language, job.language)
if job.status == 'completed' and job.transcript_text:
download_status = "Available"
else:
download_status = status_display
table_data.append([
formatted_date,
job.original_filename,
language_display,
status_display,
time_display,
job_id_display,
download_status
])
return table_data
except Exception as e:
print(f"❌ Error loading transcription history: {str(e)}")
return []
def get_ai_summary_history_table(user, show_all=False):
"""Get AI summary history table"""
if not user:
return []
try:
limit = 100 if show_all else 20
summary_jobs = ai_summary_manager.get_user_summary_history(user.user_id, limit=limit)
table_data = []
for job in summary_jobs:
try:
created_time = datetime.fromisoformat(job.created_at)
formatted_date = created_time.strftime("%Y-%m-%d %H:%M")
except:
formatted_date = job.created_at[:16]
status_display = format_status(job.status)
time_display = format_processing_time(job.created_at, job.completed_at)
job_id_display = job.job_id[:8] + "..." if len(job.job_id) > 8 else job.job_id
# Get source summary
source_summary = f"{len(job.original_files)} sources"
if len(job.original_files) <= 2:
source_summary = ", ".join([f[:20] + "..." if len(f) > 20 else f for f in job.original_files])
if job.status == 'completed' and job.summary_text:
download_status = "Available"
else:
download_status = status_display
table_data.append([
formatted_date,
source_summary,
job.settings.get('output_language', 'Thai') if job.settings else 'Thai',
status_display,
time_display,
job_id_display,
download_status
])
return table_data
except Exception as e:
print(f"❌ Error loading AI summary history: {str(e)}")
return []
def refresh_transcription_history(user, show_all=False):
"""Refresh transcription history and create download files"""
if not user:
return [], gr.update(), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
try:
table_data = get_transcription_history_table(user, show_all)
stats_display = get_user_stats_display(user)
# Get completed transcription jobs for downloads
completed_jobs = transcription_manager.get_user_history(user.user_id, limit=50)
completed_transcripts = [
job for job in completed_jobs
if job.status == 'completed' and job.transcript_text
]
# Create download files
download_updates = []
for i in range(5):
if i < len(completed_transcripts):
job = completed_transcripts[i]
try:
file_path = create_transcript_file(job.transcript_text, job.job_id)
label = f"📄 {job.original_filename} ({job.created_at[:10]})"
download_updates.append(gr.update(visible=True, value=file_path, label=label))
except Exception as e:
print(f"Error creating transcript download: {e}")
download_updates.append(gr.update(visible=False))
else:
download_updates.append(gr.update(visible=False))
return [table_data, stats_display] + download_updates
except Exception as e:
print(f"❌ Error refreshing transcription history: {str(e)}")
return [], gr.update(), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
def refresh_ai_summary_history(user, show_all=False):
"""Refresh AI summary history and create download files"""
if not user:
return [], gr.update(), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
try:
table_data = get_ai_summary_history_table(user, show_all)
stats_display = get_user_stats_display(user)
# Get completed AI summary jobs for downloads
completed_jobs = ai_summary_manager.get_user_summary_history(user.user_id, limit=50)
completed_summaries = [
job for job in completed_jobs
if job.status == 'completed' and job.summary_text
]
# Create download files
download_updates = []
for i in range(5):
if i < len(completed_summaries):
job = completed_summaries[i]
try:
file_path = create_summary_file(job.summary_text, job.job_id)
source_name = job.original_files[0][:30] if job.original_files else "AI Summary"
label = f"🤖 {source_name} ({job.created_at[:10]})"
download_updates.append(gr.update(visible=True, value=file_path, label=label))
except Exception as e:
print(f"Error creating summary download: {e}")
download_updates.append(gr.update(visible=False))
else:
download_updates.append(gr.update(visible=False))
return [table_data, stats_display] + download_updates
except Exception as e:
print(f"❌ Error refreshing AI summary history: {str(e)}")
return [], gr.update(), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
# PDPA Compliance Functions (same as before, but updated for summaries too)
def export_user_data(user):
"""Export comprehensive user data including summaries"""
if not user:
return "❌ Please log in to export your data", gr.update(visible=False)
try:
# Export transcript data
transcript_export = transcription_manager.export_user_data(user.user_id)
# Export AI summary data (if available)
try:
summary_export = {
'ai_summaries': [job.__dict__ for job in ai_summary_manager.get_user_summary_history(user.user_id, limit=1000)],
'summary_stats': transcription_manager.get_user_summary_stats(user.user_id)
}
except:
summary_export = {'ai_summaries': [], 'summary_stats': {}}
# Combine exports
combined_export = {
**transcript_export,
**summary_export,
'export_type': 'comprehensive_azure_ai_service',
'services': ['transcription', 'ai_summarization']
}
# Create export file
os.makedirs("temp", exist_ok=True)
filename = f"temp/user_data_export_{user.user_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
with open(filename, "w", encoding="utf-8") as f:
json.dump(combined_export, f, indent=2, ensure_ascii=False, default=str)
print(f"📦 [{user.username}] Comprehensive data export created")
return "✅ Your complete data (transcripts + AI summaries) has been exported successfully", gr.update(visible=True, value=filename, label="Download Your Complete Data Export")
except Exception as e:
print(f"❌ Error exporting comprehensive user data: {str(e)}")
return f"❌ Export failed: {str(e)}", gr.update(visible=False)
def update_marketing_consent(user, marketing_consent):
"""Update user's marketing consent"""
if not user:
return "❌ Please log in to update consent"
try:
success = transcription_manager.update_user_consent(user.user_id, marketing_consent)
if success:
user.marketing_consent = marketing_consent
print(f"📧 [{user.username}] Marketing consent updated: {marketing_consent}")
return f"✅ Marketing consent updated successfully"
else:
return "❌ Failed to update consent"
except Exception as e:
return f"❌ Error: {str(e)}"
def delete_user_account(user, confirmation_text):
"""Delete user account and all data (transcripts + summaries)"""
if not user:
return "❌ Please log in to delete account", None, gr.update(visible=True), gr.update(visible=False)
if confirmation_text != "DELETE MY ACCOUNT":
return "❌ Please type 'DELETE MY ACCOUNT' to confirm", user, gr.update(visible=False), gr.update(visible=True)
try:
# Delete transcript data
success = transcription_manager.delete_user_account(user.user_id)
# Delete AI summary data (if backend supports it)
try:
transcription_manager.delete_user_summary_data(user.user_id)
except Exception as e:
print(f"⚠️ Warning: Could not delete AI summary data: {e}")
if success:
print(f"🗑️ [{user.username}] Complete account deleted (transcripts + AI summaries)")
return "✅ Your account and all data (transcripts + AI summaries) have been permanently deleted", None, gr.update(visible=True), gr.update(visible=False)
else:
return "❌ Failed to delete account", user, gr.update(visible=False), gr.update(visible=True)
except Exception as e:
return f"❌ Error: {str(e)}", user, gr.update(visible=False), gr.update(visible=True)
def on_user_login(user):
"""Update UI components when user logs in"""
if user:
return gr.update(value=user.marketing_consent)
else:
return gr.update(value=False)
def create_transcript_file(transcript_text, job_id):
"""Create a downloadable transcript file"""
os.makedirs("temp", exist_ok=True)
filename = f"temp/transcript_{job_id}.txt"
with open(filename, "w", encoding="utf-8") as f:
f.write(transcript_text)
return filename
def create_summary_file(summary_text, job_id):
"""Create a downloadable AI summary file"""
os.makedirs("temp", exist_ok=True)
filename = f"temp/ai_summary_{job_id}.txt"
with open(filename, "w", encoding="utf-8") as f:
f.write(summary_text)
return filename
# Enhanced CSS with AI Summary styling
enhanced_css = """
/* Main container styling */
.gradio-container {
background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%);
font-family: 'Segoe UI', system-ui, sans-serif;
color: #212529;
}
/* Enhanced header styling */
.main-header {
background: linear-gradient(135deg, #007bff, #0056b3);
color: white;
border: none;
border-radius: 12px;
padding: 30px;
text-align: center;
margin-bottom: 20px;
box-shadow: 0 4px 12px rgba(0,123,255,0.3);
}
.main-header h1 {
color: white;
margin-bottom: 10px;
font-size: 2.5em;
font-weight: 700;
text-shadow: 0 2px 4px rgba(0,0,0,0.3);
}
.main-header p {
color: rgba(255,255,255,0.9);
font-size: 1.2em;
margin: 0;
}
/* Card styling with enhanced AI theme */
.gr-box {
background: white;
border: 1px solid #dee2e6;
border-radius: 12px;
box-shadow: 0 4px 12px rgba(0,0,0,0.08);
padding: 25px;
margin: 10px 0;
transition: all 0.3s ease;
}
.gr-box:hover {
box-shadow: 0 6px 16px rgba(0,0,0,0.12);
transform: translateY(-2px);
}
/* AI-specific card styling */
.ai-summary-card {
background: linear-gradient(135deg, #f8f9ff, #e8f2ff);
border: 2px solid #007bff;
border-radius: 12px;
padding: 25px;
margin: 15px 0;
}
/* Button styling with AI enhancements */
.gr-button {
background: linear-gradient(135deg, #007bff, #0056b3);
border: none;
border-radius: 8px;
color: white;
font-weight: 600;
padding: 14px 28px;
transition: all 0.3s ease;
box-shadow: 0 3px 6px rgba(0,123,255,0.3);
text-transform: uppercase;
letter-spacing: 0.5px;
}
.gr-button:hover {
background: linear-gradient(135deg, #0056b3, #004085);
transform: translateY(-2px);
box-shadow: 0 5px 10px rgba(0,123,255,0.4);
}
/* AI Summary specific buttons */
.ai-button {
background: linear-gradient(135deg, #28a745, #1e7e34);
}
.ai-button:hover {
background: linear-gradient(135deg, #1e7e34, #155724);
}
/* Status displays with enhanced styling */
.status-display {
background: linear-gradient(135deg, #e3f2fd, #bbdefb);
border-left: 4px solid #2196f3;
padding: 20px;
border-radius: 0 12px 12px 0;
margin: 15px 0;
font-family: 'Monaco', 'Consolas', monospace;
font-size: 14px;
line-height: 1.6;
}
.ai-status-display {
background: linear-gradient(135deg, #e8f5e8, #c8e6c9);
border-left: 4px solid #28a745;
}
/* Auto-refresh indicators */
.auto-refresh-indicator {
background: linear-gradient(135deg, #fff3cd, #ffeaa7);
border: 2px solid #ffc107;
border-radius: 8px;
padding: 10px 16px;
font-size: 12px;
color: #856404;
text-align: center;
animation: pulse 2s infinite;
font-weight: 600;
}
@keyframes pulse {
0%, 100% { opacity: 1; box-shadow: 0 0 0 0 rgba(255, 193, 7, 0.4); }
50% { opacity: 0.8; box-shadow: 0 0 0 10px rgba(255, 193, 7, 0); }
}
/* User stats with enhanced design */
.user-stats {
background: linear-gradient(135deg, #e8f5e8, #c8e6c9);
border: 2px solid #28a745;
border-radius: 8px;
padding: 12px 16px;
font-size: 13px;
color: #155724;
text-align: center;
font-weight: 600;
box-shadow: 0 2px 8px rgba(40,167,69,0.2);
}
/* Enhanced input styling */
.gr-textbox, .gr-dropdown, .gr-file {
border: 2px solid #e9ecef;
border-radius: 10px;
background: white;
color: #212529;
transition: all 0.3s ease;
padding: 12px;
font-size: 14px;
}
.gr-textbox:focus, .gr-dropdown:focus {
border-color: #007bff;
box-shadow: 0 0 0 4px rgba(0,123,255,0.1);
background: #f8f9ff;
}
/* Tab styling with AI theme */
.tab-nav {
background: white;
border-bottom: 3px solid #007bff;
border-radius: 12px 12px 0 0;
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
}
.tab-nav .tab-button {
padding: 15px 25px;
font-weight: 600;
transition: all 0.3s ease;
}
.tab-nav .tab-button.selected {
background: linear-gradient(135deg, #007bff, #0056b3);
color: white;
}
/* History table with enhanced design and black text */
.history-table {
background: white;
border: 2px solid #dee2e6;
border-radius: 12px;
font-size: 14px;
overflow: hidden;
color: #000000; /* Force black text */
}
.history-table thead th {
background: linear-gradient(135deg, #343a40, #495057);
color: white;
font-weight: 700;
padding: 16px 12px;
text-align: center;
border: none;
}
.history-table tbody tr {
transition: all 0.2s ease;
border-bottom: 1px solid #dee2e6;
color: #000000; /* Force black text for rows */
}
.history-table tbody tr:hover {
background: linear-gradient(135deg, #f8f9ff, #e8f2ff);
transform: scale(1.01);
color: #000000; /* Ensure text stays black on hover */
}
.history-table tbody td {
padding: 12px;
vertical-align: middle;
text-align: center;
border-right: 1px solid #dee2e6;
color: #000000 !important; /* Force black text with !important */
font-weight: 500;
}
.history-table tbody td:last-child {
border-right: none;
}
/* AI Summary specific elements */
.ai-content-sources {
background: linear-gradient(135deg, #fff8e1, #ffecb3);
border: 2px solid #ffa000;
border-radius: 12px;
padding: 20px;
margin: 15px 0;
}
.ai-instructions {
background: linear-gradient(135deg, #e8f5e8, #c8e6c9);
border: 2px solid #4caf50;
border-radius: 12px;
padding: 20px;
margin: 15px 0;
}
.ai-results {
background: linear-gradient(135deg, #f3e5f5, #e1bee7);
border: 2px solid #9c27b0;
border-radius: 12px;
padding: 20px;
margin: 15px 0;
}
/* Enhanced file upload areas */
.file-upload-area {
border: 3px dashed #007bff;
border-radius: 12px;
padding: 30px;
text-align: center;
background: linear-gradient(135deg, #f8f9ff, #e8f2ff);
transition: all 0.3s ease;
}
.file-upload-area:hover {
border-color: #0056b3;
background: linear-gradient(135deg, #e8f2ff, #d3e8ff);
}
/* Progress indicators */
.progress-indicator {
background: linear-gradient(90deg, #007bff, #28a745, #ffc107);
height: 4px;
border-radius: 2px;
animation: progress 2s linear infinite;
}
@keyframes progress {
0% { width: 0%; }
50% { width: 70%; }
100% { width: 100%; }
}
/* Enhanced tooltips and help text */
.help-text {
color: #6c757d;
font-style: italic;
font-size: 12px;
margin-top: 5px;
}
/* Responsive design improvements */
@media (max-width: 768px) {
.main-header h1 { font-size: 2em; }
.gr-button { padding: 10px 20px; font-size: 14px; }
.gr-box { padding: 15px; margin: 5px 0; }
}
"""
# Create the main interface
with gr.Blocks(
theme=gr.themes.Soft(
primary_hue="blue",
secondary_hue="green",
neutral_hue="gray",
font=["system-ui", "sans-serif"]
),
css=enhanced_css,
title="🎙️🤖 Azure-Powered AI Conference Service - Advanced Transcription & Intelligent Summarization"
) as demo:
# Global state
current_user = gr.State(None)
job_state = gr.State({})
summary_job_state = gr.State({})
# Header
with gr.Row():
gr.HTML("""
<div class="main-header">
<h1>🎙️🤖 Azure-Powered AI Conference Service</h1>
<p>Advanced AI-powered conference analysis with transcription, computer vision, and intelligent summarization using Azure AI Foundry</p>
</div>
""")
# User stats display
user_stats_display = gr.Textbox(
label="",
lines=1,
interactive=False,
show_label=False,
placeholder="👤 Please log in to view your comprehensive statistics...",
elem_classes=["user-stats"]
)
# Authentication Section
with gr.Column(visible=True, elem_classes=["auth-form"]) as auth_section:
gr.Markdown("## 🔐 Authentication Required")
gr.Markdown("Please log in or create an account to use the advanced AI-powered conference analysis service.")
with gr.Tabs() as auth_tabs:
# Login Tab
with gr.Tab("🔒 Login") as login_tab:
with gr.Column():
login_email = gr.Textbox(
label="Email or Username",
placeholder="Enter your email or username"
)
login_password = gr.Textbox(
label="Password",
type="password",
placeholder="Enter your password"
)
with gr.Row():
login_btn = gr.Button("🔒 Login", variant="primary", elem_classes=["auth-button"])
login_status = gr.Textbox(
label="",
show_label=False,
interactive=False,
placeholder="Enter your credentials and click Login"
)
# Register Tab
with gr.Tab("📝 Register") as register_tab:
with gr.Column():
reg_email = gr.Textbox(
label="Email",
placeholder="Enter your email address"
)
reg_username = gr.Textbox(
label="Username",
placeholder="Choose a username (3-30 characters, alphanumeric and underscore)"
)
reg_password = gr.Textbox(
label="Password",
type="password",
placeholder="Create a strong password (min 8 chars, mixed case, numbers)"
)
reg_confirm_password = gr.Textbox(
label="Confirm Password",
type="password",
placeholder="Confirm your password"
)
gr.Markdown("### 📋 Privacy & Data Consent")
with gr.Column(elem_classes=["privacy-notice"]):
gr.Markdown("""
**Enhanced Privacy Notice**: By creating an account, you acknowledge that:
- Your data will be stored securely in user-separated Azure Blob Storage
- Transcriptions are processed using Azure Speech Services
- AI summaries are generated using Azure OpenAI with advanced privacy protection
- Computer vision analysis may be performed on uploaded images/videos
- You can export or delete all your data (transcripts + AI summaries) at any time
- We comply with GDPR and data protection regulations
""")
gdpr_consent = gr.Checkbox(
label="I consent to the processing of my personal data as described in the Privacy Notice (Required)",
value=False
)
data_retention_consent = gr.Checkbox(
label="I agree to data retention for transcription and AI analysis service functionality (Required)",
value=False
)
marketing_consent = gr.Checkbox(
label="I consent to receiving marketing communications about new AI features (Optional)",
value=False
)
with gr.Row():
register_btn = gr.Button("📝 Create Account", variant="primary", elem_classes=["auth-button"])
register_status = gr.Textbox(
label="",
show_label=False,
interactive=False,
placeholder="Fill out the form and agree to the required consents to create your account"
)
login_after_register = gr.Button(
"🔒 Go to Login",
visible=False,
variant="secondary"
)
# Main Application (visible only when logged in)
with gr.Column(visible=False) as main_app:
# Logout button
with gr.Row():
with gr.Column(scale=3):
pass
with gr.Column(scale=1):
logout_btn = gr.Button("👋 Logout", variant="secondary")
# Enhanced tabs with AI Summary
with gr.Tabs():
# Transcription tab
with gr.Tab("🎙️ Transcribe"):
with gr.Row():
# Left column - Input settings
with gr.Column(scale=1):
gr.Markdown("### 📁 Upload File")
file_upload = gr.File(
label="Audio or Video File",
type="filepath",
file_types=[
".wav", ".mp3", ".ogg", ".opus", ".flac", ".wma", ".aac",
".m4a", ".amr", ".webm", ".speex",
".mp4", ".mov", ".avi", ".mkv", ".wmv", ".flv", ".3gp"
],
elem_classes=["file-upload-area"]
)
with gr.Row():
language = gr.Dropdown(
choices=[(v, k) for k, v in ALLOWED_LANGS.items()],
label="Language",
value="th-TH" # Default to Thai
)
audio_format = gr.Dropdown(
choices=AUDIO_FORMATS,
value="wav",
label="Output Format"
)
gr.Markdown("### ⚙️ Advanced Settings")
with gr.Row():
diarization_enabled = gr.Checkbox(
label="Speaker Identification",
value=True
)
speakers = gr.Slider(
minimum=1,
maximum=10,
step=1,
value=2,
label="Max Speakers"
)
with gr.Row():
timestamps = gr.Checkbox(
label="Timestamps",
value=True
)
profanity = gr.Dropdown(
choices=["masked", "removed", "raw"],
value="masked",
label="Profanity Filter"
)
with gr.Row():
punctuation = gr.Dropdown(
choices=["automatic", "dictated", "none"],
value="automatic",
label="Punctuation"
)
lexical = gr.Checkbox(
label="Lexical Form",
value=False
)
submit_btn = gr.Button(
"🚀 Start Transcription",
variant="primary",
size="lg"
)
# Right column - Results
with gr.Column(scale=1):
gr.Markdown("### 📊 Status & Results")
# Auto-refresh indicator
auto_refresh_status_display = gr.Textbox(
label="",
lines=1,
interactive=False,
show_label=False,
visible=False,
elem_classes=["auto-refresh-indicator"]
)
status_display = gr.Textbox(
label="",
lines=4,
interactive=False,
show_label=False,
placeholder="Upload a file and click 'Start Transcription' to begin...\nStatus will auto-refresh every 10 seconds during processing.\nYour data is stored in your private user folder for PDPA compliance.\nCompleted transcripts can be used for AI summarization.",
elem_classes=["status-display"]
)
job_info = gr.Textbox(
label="",
lines=1,
interactive=False,
show_label=False,
placeholder=""
)
with gr.Row():
refresh_btn = gr.Button(
"🔄 Check Status",
variant="secondary"
)
stop_refresh_btn = gr.Button(
"⏹️ Stop Auto-Refresh",
variant="secondary"
)
gr.Markdown("### 📄 Transcript Results")
transcript_output = gr.Textbox(
label="Transcript",
lines=12,
interactive=False,
placeholder="Your transcript with speaker identification and precise timestamps (HH:MM:SS) will appear here...\nThis transcript will be available for AI-powered summarization.",
elem_classes=["status-display"]
)
download_file = gr.File(
label="Download Transcript",
interactive=False,
visible=False
)
# AI Summary tab with proper structure
with gr.Tab("🤖 AI Summary Conference"):
gr.Markdown("### 🎯 AI-Powered Conference Summarization")
gr.Markdown("*Generate intelligent summaries from transcripts, documents, audio/video files, and visual content using Azure AI Foundry*")
with gr.Row():
# INPUT CONTENT Block
with gr.Column(scale=1, elem_classes=["ai-content-sources"]):
gr.Markdown("## 📂 INPUT CONTENT")
# Part 1: Audio/Video Content with Tabs
gr.Markdown("#### Audio/Video Content")
with gr.Tabs():
with gr.Tab("📜 Existing Transcripts"):
available_transcripts = gr.Dropdown(
label="Select Transcript",
choices=[],
value=None,
multiselect=True
)
refresh_transcripts_btn = gr.Button(
"🔄 Refresh Transcripts",
variant="secondary",
size="sm"
)
with gr.Tab("🎥 New Audio/Video Files"):
new_audio_video_file = gr.File(
label="Upload Audio/Video File",
file_count="single",
file_types=[
".mp4", ".mov", ".avi", ".mkv", ".webm", ".flv", ".3gp", ".wmv",
".wav", ".mp3", ".ogg", ".opus", ".flac", ".wma", ".aac", ".m4a", ".amr", ".speex"
],
elem_classes=["file-upload-area"]
)
# Part 2: Document/Image Uploads for OCR
gr.Markdown("#### Document/Image Files (OCR Processing)")
document_image_files = gr.File(
label="Upload Documents/Images for OCR Text Extraction",
file_count="multiple",
file_types=[
".pdf", ".docx", ".doc", ".pptx", ".ppt", ".xlsx", ".xls", ".txt", ".csv", ".json",
".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff", ".webp"
],
elem_classes=["file-upload-area"]
)
gr.HTML("""
<div class="help-text">
<p><strong>Documents:</strong> PDF, DOCX, DOC, PPTX, PPT, XLSX, XLS, TXT, CSV, JSON</p>
<p><strong>Images:</strong> JPG, JPEG, PNG, BMP, GIF, TIFF, WebP</p>
<p><strong>OCR Process:</strong> Extract text from documents and images for AI analysis</p>
</div>
""")
# AI Instructions
with gr.Column(scale=1, elem_classes=["ai-instructions"]):
gr.Markdown("## 🧠 AI Instructions")
gr.Markdown("#### Instructions for AI")
ai_instructions = gr.Textbox(
label="",
lines=6,
placeholder="Describe the conference type, desired format, and any corrections...\n\nExample: 'สรุปการประชุมรายไตรมาสนี้ โฟกัสที่ผลการเงิน การตัดสินใจสำคัญ และแผนงาน สร้างบทสรุปผู้บริหารพร้อมจุดสำคัญ'",
value="สรุปเนื้อหาการประชุมหรือเอกสารนี้ โดยจัดรูปแบบเป็นหัวข้อและรายละเอียดสำคัญ พร้อมระบุประเด็นที่ต้องติดตาม", # Default Thai instructions
show_label=False
)
with gr.Row():
summary_format = gr.Dropdown(
choices=["บทสรุปผู้บริหาร", "รายงานการประชุม", "แผนงานและภารกิจ", "ประเด็นสำคัญ", "การวิเคราะห์เชิงลึก"],
value="บทสรุปผู้บริหาร",
label="Format"
)
output_language = gr.Dropdown(
choices=["Thai", "English", "Spanish", "French", "German", "Chinese", "Japanese"],
value="Thai", # Default to Thai
label="Language"
)
gr.Markdown("#### Focus Areas")
focus_areas = gr.Textbox(
label="",
placeholder="เช่น ผลการเงิน การตัดสินใจ การพูดคุยด้านเทคนิค",
show_label=False
)
with gr.Row():
include_timestamps = gr.Checkbox(
label="เวลา (Timestamps)",
value=True
)
include_action_items = gr.Checkbox(
label="รายการภารกิจ",
value=True
)
generate_summary_btn = gr.Button(
"🚀 สร้าง AI Summary",
variant="primary",
size="lg",
elem_classes=["ai-button"]
)
# Status & Results
with gr.Column(scale=1, elem_classes=["ai-results"]):
gr.Markdown("## 📊 Status & Results")
ai_status_display = gr.Textbox(
label="",
lines=3,
interactive=False,
show_label=False,
placeholder="ไม่มีงาน AI Summary ที่กำลังดำเนินการ",
elem_classes=["ai-status-display"]
)
# Auto-refresh indicator for AI
ai_auto_refresh_status = gr.Textbox(
label="",
lines=1,
interactive=False,
show_label=False,
visible=False,
elem_classes=["auto-refresh-indicator"]
)
ai_job_info = gr.Textbox(
label="",
lines=1,
interactive=False,
show_label=False,
placeholder=""
)
check_ai_status_btn = gr.Button(
"🔄 Check Status",
variant="secondary"
)
gr.Markdown("#### AI Summary Results")
ai_summary_output = gr.Textbox(
label="",
lines=12,
interactive=False,
show_label=False,
placeholder="ผลลัพธ์ AI Summary พร้อมข้อมูลเชิงลึก ประเด็นสำคัญ และคำแนะนำที่สามารถนำไปปฏิบัติได้จะแสดงที่นี่...",
elem_classes=["ai-status-display"]
)
ai_download_file = gr.File(
label="Download AI Summary",
interactive=False,
visible=False
)
# Enhanced History tab with separate services
with gr.Tab("📚 My History"):
gr.Markdown("### 📋 Your Service History")
gr.Markdown("*View and download your transcription and AI summarization history (PDPA compliant - only your data)*")
# Service History Tabs
with gr.Tabs():
# Transcription History Tab
with gr.Tab("🎙️ Transcription History"):
with gr.Row():
refresh_transcription_history_btn = gr.Button(
"🔄 Refresh Transcription History",
variant="primary"
)
show_all_transcriptions_checkbox = gr.Checkbox(
label="Show All Transcription Records (not just recent 20)",
value=False
)
transcription_history_table = gr.Dataframe(
headers=["Date", "Filename", "Language", "Status", "Duration", "Job ID", "Download"],
datatype=["str", "str", "str", "str", "str", "str", "str"],
col_count=(7, "fixed"),
row_count=(20, "dynamic"),
wrap=True,
interactive=False,
elem_classes=["history-table"]
)
# Download Files Section for Transcriptions
gr.Markdown("### 📥 Download Your Transcripts")
gr.Markdown("*Your available transcript downloads will appear below after refreshing*")
with gr.Column():
transcript_download_1 = gr.File(label="", visible=False, interactive=False)
transcript_download_2 = gr.File(label="", visible=False, interactive=False)
transcript_download_3 = gr.File(label="", visible=False, interactive=False)
transcript_download_4 = gr.File(label="", visible=False, interactive=False)
transcript_download_5 = gr.File(label="", visible=False, interactive=False)
# AI Summary History Tab
with gr.Tab("🤖 AI Summary History"):
with gr.Row():
refresh_ai_summary_history_btn = gr.Button(
"🔄 Refresh AI Summary History",
variant="primary"
)
show_all_summaries_checkbox = gr.Checkbox(
label="Show All AI Summary Records (not just recent 20)",
value=False
)
ai_summary_history_table = gr.Dataframe(
headers=["Date", "Sources", "Language", "Status", "Duration", "Job ID", "Download"],
datatype=["str", "str", "str", "str", "str", "str", "str"],
col_count=(7, "fixed"),
row_count=(20, "dynamic"),
wrap=True,
interactive=False,
elem_classes=["history-table"]
)
# Download Files Section for AI Summaries
gr.Markdown("### 📥 Download Your AI Summaries")
gr.Markdown("*Your available AI summary downloads will appear below after refreshing*")
with gr.Column():
summary_download_1 = gr.File(label="", visible=False, interactive=False)
summary_download_2 = gr.File(label="", visible=False, interactive=False)
summary_download_3 = gr.File(label="", visible=False, interactive=False)
summary_download_4 = gr.File(label="", visible=False, interactive=False)
summary_download_5 = gr.File(label="", visible=False, interactive=False)
# Enhanced Privacy & Data tab
with gr.Tab("🔒 Privacy & Data"):
gr.Markdown("### 🔒 Enhanced GDPR & Data Protection")
gr.Markdown("Manage your personal data and privacy settings for both transcription and AI services in compliance with data protection regulations.")
with gr.Column(elem_classes=["pdpa-section"]):
gr.Markdown("#### 📊 Complete Data Export")
gr.Markdown("Download all your personal data including transcriptions, AI summaries, account info, and usage statistics.")
export_btn = gr.Button("📦 Export My Complete Data", variant="primary")
export_status = gr.Textbox(
label="",
show_label=False,
interactive=False,
placeholder="Click 'Export My Complete Data' to download your comprehensive data archive (transcripts + AI summaries)"
)
export_file = gr.File(
label="Your Complete Data Export",
visible=False,
interactive=False
)
with gr.Column(elem_classes=["pdpa-section"]):
gr.Markdown("#### 📧 Marketing Consent")
gr.Markdown("Update your preferences for receiving marketing communications about new AI features.")
marketing_consent_checkbox = gr.Checkbox(
label="I consent to receiving marketing communications about new AI features",
value=False
)
update_consent_btn = gr.Button("✅ Update Consent", variant="secondary")
consent_status = gr.Textbox(
label="",
show_label=False,
interactive=False,
placeholder="Update your marketing consent preferences"
)
with gr.Column(elem_classes=["pdpa-section"]):
gr.Markdown("#### ⚠️ Complete Account Deletion")
gr.Markdown("""
**Warning**: This action is irreversible and will permanently delete:
- Your user account and profile
- All transcription history and files
- All AI summary history and results
- All data stored in Azure Blob Storage
- Usage statistics and preferences
- Any stored AI model interactions
""")
deletion_confirmation = gr.Textbox(
label="Type 'DELETE MY ACCOUNT' to confirm",
placeholder="Type the exact phrase to confirm complete account deletion"
)
delete_account_btn = gr.Button(
"🗑️ Delete My Complete Account",
variant="stop",
elem_classes=["danger-button"]
)
deletion_status = gr.Textbox(
label="",
show_label=False,
interactive=False,
placeholder="Complete account deletion requires confirmation text"
)
# Auto-refresh timers
transcript_timer = gr.Timer(10.0)
ai_timer = gr.Timer(10.0)
# Event handlers
# Authentication events (same as before)
login_btn.click(
login_user,
inputs=[login_email, login_password],
outputs=[login_status, current_user, auth_section, main_app, user_stats_display]
).then(
on_user_login,
inputs=[current_user],
outputs=[marketing_consent_checkbox]
).then(
lambda user: ("", "") if user else (gr.update(), gr.update()),
inputs=[current_user],
outputs=[login_email, login_password]
).then(
get_available_transcripts,
inputs=[current_user],
outputs=[available_transcripts]
)
register_btn.click(
register_user,
inputs=[reg_email, reg_username, reg_password, reg_confirm_password,
gdpr_consent, data_retention_consent, marketing_consent],
outputs=[register_status, login_after_register]
).then(
lambda status: ("", "", "", "", False, False, False) if "✅" in status else (gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()),
inputs=[register_status],
outputs=[reg_email, reg_username, reg_password, reg_confirm_password, gdpr_consent, data_retention_consent, marketing_consent]
)
login_after_register.click(
lambda: (gr.update(selected=0), ""),
outputs=[auth_tabs, register_status]
)
logout_btn.click(
logout_user,
outputs=[current_user, login_status, auth_section, main_app, user_stats_display]
)
# Transcription events
submit_btn.click(
submit_transcription,
inputs=[
file_upload, language, audio_format, diarization_enabled,
speakers, profanity, punctuation, timestamps, lexical, current_user
],
outputs=[status_display, transcript_output, download_file, job_info, job_state, auto_refresh_status_display, user_stats_display]
)
refresh_btn.click(
check_current_job_status,
inputs=[job_state, current_user],
outputs=[status_display, transcript_output, download_file, job_info, auto_refresh_status_display, user_stats_display]
)
# AI Summary events - simplified structure
refresh_transcripts_btn.click(
lambda user: gr.update(choices=[(job.original_filename + f" ({job.created_at[:16]})", job.job_id)
for job in transcription_manager.get_user_history(user.user_id, limit=50)
if job.status == 'completed' and job.transcript_text] if user else []),
inputs=[current_user],
outputs=[available_transcripts]
)
generate_summary_btn.click(
submit_ai_summary_enhanced,
inputs=[
available_transcripts, new_audio_video_file, document_image_files,
ai_instructions, summary_format, output_language, focus_areas,
include_timestamps, include_action_items, current_user
],
outputs=[ai_status_display, ai_summary_output, ai_download_file, ai_job_info, summary_job_state, ai_auto_refresh_status, user_stats_display]
)
check_ai_status_btn.click(
check_ai_summary_status,
inputs=[summary_job_state, current_user],
outputs=[ai_status_display, ai_summary_output, ai_download_file, ai_job_info, ai_auto_refresh_status, user_stats_display]
)
# Auto-refresh timer events
transcript_timer.tick(
auto_refresh_status,
inputs=[job_state, current_user],
outputs=[status_display, transcript_output, download_file, job_info, auto_refresh_status_display, user_stats_display]
)
ai_timer.tick(
auto_refresh_ai_summary,
inputs=[summary_job_state, current_user],
outputs=[ai_status_display, ai_summary_output, ai_download_file, ai_job_info, ai_auto_refresh_status, user_stats_display]
)
# History events - Separate for each service with downloads
refresh_transcription_history_btn.click(
refresh_transcription_history,
inputs=[current_user, show_all_transcriptions_checkbox],
outputs=[transcription_history_table, user_stats_display, transcript_download_1, transcript_download_2, transcript_download_3, transcript_download_4, transcript_download_5]
)
show_all_transcriptions_checkbox.change(
refresh_transcription_history,
inputs=[current_user, show_all_transcriptions_checkbox],
outputs=[transcription_history_table, user_stats_display, transcript_download_1, transcript_download_2, transcript_download_3, transcript_download_4, transcript_download_5]
)
refresh_ai_summary_history_btn.click(
refresh_ai_summary_history,
inputs=[current_user, show_all_summaries_checkbox],
outputs=[ai_summary_history_table, user_stats_display, summary_download_1, summary_download_2, summary_download_3, summary_download_4, summary_download_5]
)
show_all_summaries_checkbox.change(
refresh_ai_summary_history,
inputs=[current_user, show_all_summaries_checkbox],
outputs=[ai_summary_history_table, user_stats_display, summary_download_1, summary_download_2, summary_download_3, summary_download_4, summary_download_5]
)
# PDPA compliance events
export_btn.click(
export_user_data,
inputs=[current_user],
outputs=[export_status, export_file]
)
update_consent_btn.click(
update_marketing_consent,
inputs=[current_user, marketing_consent_checkbox],
outputs=[consent_status]
)
delete_account_btn.click(
delete_user_account,
inputs=[current_user, deletion_confirmation],
outputs=[deletion_status, current_user, auth_section, main_app]
)
# Auto-hide/show speakers slider
diarization_enabled.change(
lambda enabled: gr.update(visible=enabled),
inputs=[diarization_enabled],
outputs=[speakers]
)
# Load initial data
demo.load(
lambda: (
print("🚀 Azure-Powered AI Conference Service Started..."),
get_user_stats_display(None)
)[1],
outputs=[user_stats_display]
)
# Enhanced info section
with demo:
gr.HTML("""
<div style="background: linear-gradient(135deg, #ffffff, #f8f9fa); border: 2px solid #007bff; border-radius: 16px; padding: 25px; margin-top: 20px; color: #212529; box-shadow: 0 4px 12px rgba(0,123,255,0.1);">
<h3 style="color: #007bff; margin-top: 0; font-size: 1.5em;">📋 How to Use the Advanced AI Service</h3>
<ol style="line-height: 1.8; font-size: 14px;">
<li><strong>🔐 Register/Login:</strong> Create an account or log in with existing credentials</li>
<li><strong>🎙️ Transcribe:</strong> Upload audio/video files for high-quality transcription with speaker identification</li>
<li><strong>🤖 AI Analysis:</strong> Choose from 3 content types: existing transcripts, new audio/video, or OCR images</li>
<li><strong>📊 Monitor:</strong> Status auto-updates every 10 seconds with real-time progress</li>
<li><strong>📥 Download:</strong> Get transcripts and AI summaries with comprehensive insights</li>
<li><strong>🔒 Manage:</strong> Use Privacy & Data tab to export or delete all your data</li>
</ol>
<h3 style="color: #007bff; font-size: 1.4em;">🆕 Enhanced AI Summary Features</h3>
<div style="display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 15px; margin: 15px 0;">
<div>
<p><strong>📜 Existing Transcripts:</strong> Select from your completed transcriptions</p>
<p><strong>🎥 New Audio/Video:</strong> Upload files for transcription + AI analysis</p>
</div>
<div>
<p><strong>🖼️ OCR Images:</strong> Extract text from images using computer vision</p>
<p><strong>🇹🇭 Thai Default:</strong> Optimized for Thai language processing</p>
</div>
<div>
<p><strong>📚 Separate History:</strong> Independent tracking for each service</p>
<p><strong>🔄 Auto-Refresh:</strong> Real-time status updates for all processes</p>
</div>
</div>
<h3 style="color: #007bff; font-size: 1.4em;">🎵 Enhanced File Support</h3>
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px; margin: 15px 0;">
<div>
<p><strong>🔹 Video:</strong> MP4, MOV, AVI, MKV, WMV, FLV, 3GP, WebM</p>
<p><strong>🎵 Audio:</strong> WAV, MP3, OGG, OPUS, FLAC, WMA, AAC, M4A</p>
</div>
<div>
<p><strong>📄 Documents:</strong> PDF, DOCX, DOC, PPTX, PPT, XLSX, XLS, CSV, TXT</p>
<p><strong>🖼️ Images:</strong> JPG, JPEG, PNG, BMP, GIF, TIFF, WebP (with OCR)</p>
</div>
</div>
<h3 style="color: #007bff; font-size: 1.4em;">💡 Pro Tips for Best Results</h3>
<div style="background: linear-gradient(135deg, #e8f4f8, #d4e8fc); border-left: 4px solid #007bff; padding: 15px; border-radius: 8px; margin: 15px 0;">
<ul style="line-height: 1.7; font-size: 13px; margin: 0;">
<li><strong>🎙️ Transcription:</strong> WAV files process fastest, enable speaker ID for meetings</li>
<li><strong>🤖 AI Summaries:</strong> Provide detailed Thai instructions for better results</li>
<li><strong>📜 Use Tabs:</strong> Switch between content types easily with the new tab interface</li>
<li><strong>🖼️ OCR Processing:</strong> Upload clear images for best text extraction results</li>
<li><strong>📚 History:</strong> Use separate tabs to track transcription and AI summary history</li>
<li><strong>🇹🇭 Thai Language:</strong> Both services now default to Thai for optimal local use</li>
<li><strong>📊 Auto-Updates:</strong> Let the system update automatically during processing</li>
<li><strong>🔒 Privacy:</strong> Export your data regularly and manage consent preferences</li>
</ul>
</div>
<div style="text-align: center; margin-top: 20px; padding: 15px; background: linear-gradient(135deg, #007bff, #28a745); color: white; border-radius: 8px;">
<p style="margin: 0; font-weight: 600; font-size: 14px;">🚀 Powered by Azure AI Foundry | 🔒 Enterprise-Grade Security | 🌐 GDPR Compliant | 🇹🇭 Thai Language Optimized</p>
</div>
</div>
""")
if __name__ == "__main__":
print("🚀 Starting Advanced Azure-Powered AI Conference Service...")
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
show_error=True
) |