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import time
import tempfile
import traceback
import numpy as np
import soundfile as sf
import gradio as gr
# --- Pipeline & Model Imports ---
# (Kept intact to connect with your backend framework logic)
from interface import (
load_patient_registry, save_patient_registry, separate_audio, monitoring_table_rows, _copy_audio_to_storage,
run_end_to_end, search_history_records, search_reasoning_records, _initialize_models_for_live_processing, RESULTS_DIR,
process_audio_chunk_for_separation, infer_on_separated_chunk, _live_sr,
LIVE_PROCESSING_WINDOW_SECONDS, LIVE_OVERLAP_SECONDS, resolve_patient_names, save_audio_file, predict_sources
)
from gnn import EnhancedPatientStateManager, ClinicalAlertSystem
from notifications import build_live_notification_html
# ==========================================
# 1. STYLING CONFIGURATION
# ==========================================
def load_css():
css_path = os.path.join(os.path.dirname(__file__), "style.css")
if os.path.exists(css_path):
with open(css_path, "r") as f:
return f.read()
return ""
css_styles = load_css()
# ==========================================
# 2. CORE BUSINESS & UTILITY LOGIC
# ==========================================
def register_patients(ref_audio_1, ref_audio_2, ref_audio_3, patient_name_1, patient_name_2, patient_name_3):
raw_names = [patient_name_1, patient_name_2, patient_name_3]
audio_paths = [ref_audio_1, ref_audio_2, ref_audio_3]
patient_entries = []
for idx, (name, audio_path) in enumerate(zip(raw_names, audio_paths), start=1):
entry = {
"patient_id": f"patient_{idx}",
"name": name.strip() if name else f"Patient {idx}",
"reference_audio": str(audio_path) if audio_path else None,
}
patient_entries.append(entry)
save_result, error_message = save_patient_registry(patient_entries)
if error_message:
message = error_message
choices = get_registered_patient_choices()
selected_values = [choices[i + 1] if i + 1 < len(choices) else "Unassigned" for i in range(6)]
return (
message,
gr.update(choices=choices, value=selected_values[0]),
gr.update(choices=choices, value=selected_values[1]),
gr.update(choices=choices, value=selected_values[2]),
gr.update(choices=choices, value=selected_values[0]),
gr.update(choices=choices, value=selected_values[1]),
gr.update(choices=choices, value=selected_values[2]),
)
registered = [f"Patient {idx}: {entry['name']}" for idx, entry in enumerate(patient_entries, start=1)]
message = (
"Patients registered successfully. Reference audio files are saved for each patient.\n"
+ "\n".join(registered)
+ "\nSaved to pipeline_results/patient_registry.json"
)
choices = get_registered_patient_choices()
selected_values = [choices[i + 1] if i + 1 < len(choices) else "Unassigned" for i in range(6)]
return (
message,
gr.update(choices=choices, value=selected_values[0]),
gr.update(choices=choices, value=selected_values[1]),
gr.update(choices=choices, value=selected_values[2]),
gr.update(choices=choices, value=selected_values[0]),
gr.update(choices=choices, value=selected_values[1]),
gr.update(choices=choices, value=selected_values[2]),
)
def get_registered_patient_choices(default_count=3):
registry = load_patient_registry()
names = [entry.get("name") or f"Patient {idx + 1}" for idx, entry in enumerate(registry)]
names = list(dict.fromkeys(names))
if not names:
names = [f"Patient {i}" for i in range(1, default_count + 1)]
return ["Unassigned"] + names
def filter_patient_choices(query, default_count=3):
if query is None:
return gr.update(choices=[], value=None)
query_text = str(query).strip()
if not query_text:
return gr.update(choices=[], value=None)
choices = get_registered_patient_choices(default_count)
query_lower = query_text.lower()
filtered = [name for name in choices if name != "Unassigned" and name.lower().startswith(query_lower)]
return gr.update(choices=filtered)
def get_selected_patient_name(value, default_count=3):
if not value:
return None
if isinstance(value, list):
return value[0] if value else None
return value
def normalize_live_patient_names(selected_names):
normalized = []
for name in selected_names:
if name and name != "Unassigned":
normalized.append(name)
else:
normalized.append(None)
return normalized
def round_monitor_value(value):
if value is None:
return None
if isinstance(value, (int, float)):
return round(value, 2)
try:
return round(float(value), 2)
except (ValueError, TypeError):
return value
def update_separation_output_labels(p1, p2, p3):
selected = [get_selected_patient_name(p1), get_selected_patient_name(p2), get_selected_patient_name(p3)]
audio_updates = []
wave_updates = []
for i, name in enumerate(selected, start=1):
if not name or name == "Unassigned":
display = f"Patient {i}"
else:
display = name
audio_updates.append(gr.update(label=f"{display} Audio"))
wave_updates.append(gr.update(label=f"{display} Waveform"))
return (
audio_updates[0],
audio_updates[1],
audio_updates[2],
wave_updates[0],
wave_updates[1],
wave_updates[2],
)
def update_live_monitor_output_labels(p1, p2, p3):
selected = [get_selected_patient_name(p1), get_selected_patient_name(p2), get_selected_patient_name(p3)]
updates = []
for i, name in enumerate(selected, start=1):
if not name or name == "Unassigned":
display = f"Live Patient {i}"
else:
display = name
updates.append(gr.update(label=display))
return updates[0], updates[1], updates[2]
def predict(mix_audio, p1, p2, p3): # p1, p2, p3 are patient names
selected_names = [get_selected_patient_name(n) for n in [p1, p2, p3]]
selected_names = [n if n and n != "Unassigned" else None for n in selected_names]
registry = load_patient_registry()
reference_audio_paths = [None, None, None]
for i, name in enumerate(selected_names):
if name:
for entry in registry:
if entry.get("name") == name:
reference_audio_paths[i] = entry.get("local_reference_audio") or entry.get("reference_audio")
break
try:
outputs, reasoning_summaries, history_record = run_end_to_end(
mix_audio,
patient_names=selected_names,
reference_audio_paths=reference_audio_paths
)
except Exception as exc:
error_message = f"Pipeline error: {exc}"
error_trace = traceback.format_exc()
empty = [None, None, None, None, None, None]
return empty + [error_message, [[]], f"Error at {time.strftime('%H:%M:%S')}", error_trace]
monitor_rows = monitoring_table_rows()
history_status = f"Completed at {history_record.get('timestamp', 'unknown')}. {len(reasoning_summaries)} patient(s) processed."
message = f"Full pipeline complete. {len(reasoning_summaries)} reasoning summaries available."
return outputs + [message] + [monitor_rows] + [history_status]
def refresh_monitoring():
rows = monitoring_table_rows()
if not rows:
return [], "No reasoning summary available. Run the pipeline and make sure pipeline_results/reasoning_summary.json exists."
return rows, f"Loaded {len(rows)} patient states from reasoning summary."
def search_history(query):
if not query:
return [], "Enter a patient name or ID to search history."
rows = search_reasoning_records(query)
if not rows:
return [], f"No clinical findings found matching '{query}'."
return rows, f"Found {len(rows)} matching clinical record(s)."
# ==========================================
# 3. LIVE STREAMING CONTROLLERS
# ==========================================
def start_live_monitoring_session(selected_patient_1, selected_patient_2, selected_patient_3):
global _live_processor, _live_wav2vec_model, _live_gnn_model, _live_device
_live_processor, _live_wav2vec_model, _live_gnn_model, _live_device = _initialize_models_for_live_processing()
selected_names = normalize_live_patient_names([selected_patient_1, selected_patient_2, selected_patient_3])
managers = [
EnhancedPatientStateManager(),
EnhancedPatientStateManager(),
EnhancedPatientStateManager(),
]
empty_audio = np.array([], dtype=np.float32)
empty_buffers = [np.array([], dtype=np.float32) for _ in range(3)]
empty_table = []
status_names = [name for name in selected_names if name is not None]
if status_names:
status = f"Live monitoring initialized for: {', '.join(status_names)}. Click microphone to start."
else:
status = "No patients selected. Select at least one patient to monitor."
return (
empty_audio,
empty_buffers,
managers,
[0.0, 0.0, 0.0],
empty_table,
selected_names,
status,
gr.update(value=None, interactive=True),
gr.update(interactive=False),
build_live_notification_html(empty_table, selected_names),
)
def process_live_audio_stream(audio_chunk, live_audio_buffer, live_separated_buffers, live_patient_managers, live_current_timestamps, live_patient_names):
if audio_chunk is None:
return (
live_audio_buffer,
live_separated_buffers,
live_patient_managers,
live_current_timestamps,
[],
"No audio received.",
None,
None,
None,
live_patient_names or [None, None, None],
build_live_notification_html([], live_patient_names),
)
import librosa
import torch
sr, np_audio = audio_chunk
if sr is None or np_audio is None:
return (
live_audio_buffer,
live_separated_buffers,
live_patient_managers,
live_current_timestamps,
[],
"Invalid audio chunk received.",
None,
None,
None,
live_patient_names or [None, None, None],
build_live_notification_html([], live_patient_names),
)
mono = np.mean(np_audio, axis=-1) if np_audio.ndim > 1 else np_audio
target_sr = _live_sr
if sr != target_sr:
mono = librosa.resample(mono.astype(np.float32), orig_sr=sr, target_sr=target_sr)
mono = mono.astype(np.float32)
current_audio_base = live_audio_buffer if live_audio_buffer is not None else np.array([], dtype=np.float32)
current_audio = np.concatenate([current_audio_base, mono]) if current_audio_base.size > 0 else mono
max_buffer = int(LIVE_PROCESSING_WINDOW_SECONDS * target_sr)
if current_audio.size > max_buffer:
current_audio = current_audio[-max_buffer:]
window_samples = int(LIVE_PROCESSING_WINDOW_SECONDS * target_sr)
overlap_samples = int(LIVE_OVERLAP_SECONDS * target_sr)
step = window_samples - overlap_samples
new_buffers = [buf.copy() for buf in live_separated_buffers] if live_separated_buffers else [np.array([], dtype=np.float32) for _ in range(3)]
new_timestamps = list(live_current_timestamps)
audios_out = [None, None, None]
active_slots = [i for i, name in enumerate(live_patient_names) if name is not None] if live_patient_names else []
if current_audio.size >= window_samples and active_slots:
chunk = current_audio[-window_samples:]
prediction, _ = predict_sources(torch.from_numpy(chunk).unsqueeze(0), _live_sr)
separated = prediction.cpu().numpy()
for i in range(min(3, separated.shape[0])):
selected_name = live_patient_names[i] if i < len(live_patient_names) else None
if selected_name is None:
new_buffers[i] = np.array([], dtype=np.float32)
audios_out[i] = None
continue
separated_i = separated[i].astype(np.float32)
hop = step if step > 0 else window_samples
if separated_i.size > hop:
new_buffers[i] = separated_i[-hop:]
else:
new_buffers[i] = separated_i
new_timestamps[i] = new_timestamps[i] + hop / target_sr if new_timestamps[i] > 0 else hop / target_sr
# Performance Note: Disk I/O (sf.write/_copy_audio_to_storage) removed to reduce live latency
try:
audios_out[i] = (target_sr, separated_i)
except Exception:
audios_out[i] = None
rows = []
for i, manager in enumerate(live_patient_managers):
selected_name = live_patient_names[i] if i < len(live_patient_names) else None
if selected_name is None or manager is None:
continue
separated_chunk = new_buffers[i]
timestamp = new_timestamps[i] if new_timestamps[i] > 0 else 0.0
if separated_chunk.size >= target_sr:
state = infer_on_separated_chunk(
separated_chunk,
_live_gnn_model,
_live_processor,
_live_wav2vec_model,
_live_device,
manager,
f"live_patient_{i+1}",
timestamp,
)
rows.append([
selected_name,
round_monitor_value(manager.patient_data.get(f"live_patient_{i+1}", {}).get("wheeze_ema")),
round_monitor_value(manager.patient_data.get(f"live_patient_{i+1}", {}).get("crackle_ema")),
round_monitor_value(state.get("breathing_rate_mean")),
state.get("comment", ""),
])
return current_audio, new_buffers, live_patient_managers, new_timestamps, rows, "Processing live audio...", audios_out[0], audios_out[1], audios_out[2], live_patient_names, build_live_notification_html(rows, live_patient_names)
def stop_live_monitoring_session():
empty_audio = np.array([], dtype=np.float32)
empty_buffers = [np.array([], dtype=np.float32) for _ in range(3)]
cleared_managers = [None, None, None]
cleared_timestamps = [0.0, 0.0, 0.0]
return empty_audio, empty_buffers, cleared_managers, cleared_timestamps, [], "Live monitoring stopped.", gr.update(value=None, interactive=False), gr.update(interactive=True), None, None, None, build_live_notification_html([], [None, None, None])
# ==========================================
# 4. INTERFACE BUILDING METHOD
# ==========================================
def create_ui():
# Pass structural embedded CSS string variable safely inside Blocks
with gr.Blocks() as demo:
gr.HTML("<div class='header-box'><h1>Patient Monitoring System</h1></div>")
with gr.Row():
# Sidebar Menu
with gr.Column(scale=1, variant="panel"):
gr.Markdown("### Navigation")
btn_register = gr.Button("Register Patients", variant="secondary", elem_classes="sidebar-btn")
btn_separation = gr.Button("Audio Separation", variant="secondary", elem_classes="sidebar-btn")
btn_live_mon = gr.Button("Live Monitoring", variant="secondary", elem_classes="sidebar-btn")
btn_history = gr.Button("View History", variant="secondary", elem_classes="sidebar-btn")
monitor_alerts_sidebar = gr.HTML(value=build_live_notification_html([], [None, None, None]))
# Content Area
with gr.Column(scale=4):
live_audio_buffer_state = gr.State(value=None)
live_separated_buffers_state = gr.State(value=[])
live_patient_managers_state = gr.State(value=[None, None, None])
live_current_timestamps_state = gr.State(value=[0.0, 0.0, 0.0])
live_patient_names_state = gr.State(value=[None, None, None])
# Registration Page
with gr.Column(visible=True) as reg_page:
gr.Markdown("### Patient Registration")
with gr.Row():
with gr.Column(variant="panel"):
gr.Markdown("#### Patient 1")
patient_name_1 = gr.Textbox(label="Name", placeholder="Enter name", elem_classes="vibrant-status")
ref_audio_1 = gr.Audio(label="Ref Audio", type="filepath")
with gr.Column(variant="panel"):
gr.Markdown("#### Patient 2")
patient_name_2 = gr.Textbox(label="Name", placeholder="Enter name", elem_classes="vibrant-status")
ref_audio_2 = gr.Audio(label="Ref Audio", type="filepath")
with gr.Column(variant="panel"):
gr.Markdown("#### Patient 3")
patient_name_3 = gr.Textbox(label="Name", placeholder="Enter name", elem_classes="vibrant-status")
ref_audio_3 = gr.Audio(label="Ref Audio", type="filepath")
register_btn = gr.Button("Submit Registration", variant="primary", size="lg")
register_status = gr.Textbox(label="Status", interactive=False, elem_classes="vibrant-status")
# Separation Page
with gr.Column(visible=False) as sep_page:
gr.Markdown("### Source Separation & Inference")
with gr.Row():
with gr.Column(scale=2, variant="panel"):
mix_audio = gr.Audio(label="Upload Mixture (Multiple Patients)", type="filepath")
gr.Markdown("#### Patient Assignment")
with gr.Row():
sep_p1 = gr.Dropdown(
label="Source 1",
choices=[],
value=None,
interactive=True,
allow_custom_value=True,
)
sep_p2 = gr.Dropdown(
label="Source 2",
choices=[],
value=None,
interactive=True,
allow_custom_value=True,
)
sep_p3 = gr.Dropdown(
label="Source 3",
choices=[],
value=None,
interactive=True,
allow_custom_value=True,
)
submit_btn = gr.Button("Run Separation Pipeline", variant="primary")
with gr.Column(scale=1):
status_text = gr.Textbox(label="Process Status", interactive=False, elem_classes="vibrant-status")
history_status_text = gr.Textbox(label="History Logging", interactive=False, elem_classes="vibrant-status")
gr.Markdown("#### Separated Patient Data")
with gr.Row():
with gr.Column(variant="panel"):
out_audio_1 = gr.Audio(label="Patient 1 Audio", type="filepath")
out_wave_1 = gr.Image(label="Waveform 1", type="filepath")
with gr.Column(variant="panel"):
out_audio_2 = gr.Audio(label="Patient 2 Audio", type="filepath")
out_wave_2 = gr.Image(label="Waveform 2", type="filepath")
with gr.Column(variant="panel"):
out_audio_3 = gr.Audio(label="Patient 3 Audio", type="filepath")
out_wave_3 = gr.Image(label="Waveform 3", type="filepath")
gr.Markdown("#### Immediate Findings")
monitor_table_small = gr.Dataframe(
headers=["Patient Name", "mean_wheeze_prob", "mean_crackle_prob", "breathing_rate_mean", "comment"],
datatype=["str", "number", "number", "number", "str"],
interactive=False,
)
# History Page
with gr.Column(visible=False) as history_page:
gr.Markdown('<div class="page-title">Patient History Search</div>', elem_classes="page-container")
with gr.Row():
search_query = gr.Textbox(label="Search by Patient Name or Audio ID", placeholder="Enter name...", elem_classes="vibrant-status")
search_button = gr.Button("Search History", variant="primary")
history_results = gr.Dataframe(
headers=["Patient Names", "Overall", "mean_wheeze_prob", "mean_crackle_prob", "breathing_rate_mean", "comment"],
datatype=["str", "str", "str", "number"],
interactive=False,
)
history_msg = gr.Textbox(label="Search Results", interactive=False, elem_classes="vibrant-status")
# Live Monitoring Page
with gr.Column(visible=False) as live_mon_page:
gr.Markdown("## Live Audio Monitoring")
# Start/Stop buttons at the very top alone
with gr.Row():
start_live_btn = gr.Button("Start Live Monitoring", variant="primary")
stop_live_btn = gr.Button("Stop Live Monitoring", variant="stop")
# Monitor Slots row - Stretching along one line
with gr.Row():
select_patient_1 = gr.Dropdown(
label="Monitor Slot 1",
choices=[],
value=None,
interactive=True,
allow_custom_value=True,
)
select_patient_2 = gr.Dropdown(
label="Monitor Slot 2",
choices=[],
value=None,
interactive=True,
allow_custom_value=True,
)
select_patient_3 = gr.Dropdown(
label="Monitor Slot 3",
choices=[],
value=None,
interactive=True,
allow_custom_value=True,
)
with gr.Row():
with gr.Column(scale=1):
live_mic_input = gr.Audio(
sources=["microphone"],
streaming=True,
label="Live Microphone Input",
type="numpy",
interactive=True,
)
with gr.Column(scale=2):
gr.Markdown("#### Live Separated Sources")
with gr.Row():
live_out_audio_1 = gr.Audio(label="Live Patient 1", interactive=False, type="numpy")
live_out_audio_2 = gr.Audio(label="Live Patient 2", interactive=False, type="numpy")
live_out_audio_3 = gr.Audio(label="Live Patient 3", interactive=False, type="numpy")
gr.Markdown("#### Live Findings")
live_monitor_table = gr.Dataframe(
headers=["Patient Name", "mean_wheeze_prob", "mean_crackle_prob", "breathing_rate_mean", "comment"],
datatype=["str", "number", "number", "number", "str"],
interactive=False,
)
live_monitor_status = gr.Textbox(label="Live Status", interactive=False, elem_classes="vibrant-status")
# --- Tab Routing Mechanics ---
def nav_reg():
return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(variant="primary"), gr.update(variant="secondary"), gr.update(variant="secondary"), gr.update(variant="secondary")
def nav_sep():
return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(variant="secondary"), gr.update(variant="primary"), gr.update(variant="secondary"), gr.update(variant="secondary"), gr.update(choices=[], value=None), gr.update(choices=[], value=None), gr.update(choices=[], value=None)
def nav_his():
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(variant="secondary"), gr.update(variant="secondary"), gr.update(variant="secondary"), gr.update(variant="primary")
def nav_live():
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(variant="secondary"), gr.update(variant="secondary"), gr.update(variant="primary"), gr.update(variant="secondary"), gr.update(choices=[], value=None), gr.update(choices=[], value=None), gr.update(choices=[], value=None)
btn_register.click(
nav_reg,
outputs=[reg_page, sep_page, history_page, live_mon_page, btn_register, btn_separation, btn_history, btn_live_mon],
queue=False,
)
btn_separation.click(
nav_sep,
outputs=[reg_page, sep_page, history_page, live_mon_page, btn_register, btn_separation, btn_history, btn_live_mon, sep_p1, sep_p2, sep_p3],
queue=False,
)
btn_history.click(
nav_his,
outputs=[reg_page, sep_page, history_page, live_mon_page, btn_register, btn_separation, btn_history, btn_live_mon],
queue=False,
)
btn_live_mon.click(
nav_live,
outputs=[reg_page, sep_page, history_page, live_mon_page, btn_register, btn_separation, btn_history, btn_live_mon, select_patient_1, select_patient_2, select_patient_3],
queue=False,
)
# --- Interactive Trigger Bindings ---
register_btn.click(
fn=register_patients,
inputs=[ref_audio_1, ref_audio_2, ref_audio_3, patient_name_1, patient_name_2, patient_name_3],
outputs=[register_status, select_patient_1, select_patient_2, select_patient_3, sep_p1, sep_p2, sep_p3],
)
submit_btn.click(
fn=predict,
inputs=[mix_audio, sep_p1, sep_p2, sep_p3],
outputs=[out_audio_1, out_audio_2, out_audio_3, out_wave_1, out_wave_2, out_wave_3, status_text, monitor_table_small, history_status_text],
)
for dropdown in [sep_p1, sep_p2, sep_p3]:
dropdown.change(
fn=update_separation_output_labels,
inputs=[sep_p1, sep_p2, sep_p3],
outputs=[out_audio_1, out_audio_2, out_audio_3, out_wave_1, out_wave_2, out_wave_3,],
queue=False,
)
for dropdown in [select_patient_1, select_patient_2, select_patient_3]:
dropdown.change(
fn=update_live_monitor_output_labels,
inputs=[select_patient_1, select_patient_2, select_patient_3],
outputs=[live_out_audio_1, live_out_audio_2, live_out_audio_3],
queue=False,
)
for dropdown in [sep_p1, sep_p2, sep_p3, select_patient_1, select_patient_2, select_patient_3]:
dropdown.input(
fn=filter_patient_choices,
inputs=[dropdown],
outputs=[dropdown],
queue=False,
)
def clear_choices_after_select(dropdown):
return gr.update(choices=[])
for dropdown in [sep_p1, sep_p2, sep_p3, select_patient_1, select_patient_2, select_patient_3]:
dropdown.select(
fn=clear_choices_after_select,
inputs=[dropdown],
outputs=[dropdown],
queue=False,
)
search_button.click(
fn=search_history,
inputs=[search_query],
outputs=[history_results, history_msg],
)
start_live_btn.click(
fn=start_live_monitoring_session,
inputs=[select_patient_1, select_patient_2, select_patient_3],
outputs=[
live_audio_buffer_state,
live_separated_buffers_state,
live_patient_managers_state,
live_current_timestamps_state,
live_monitor_table,
live_patient_names_state,
live_monitor_status,
live_mic_input,
start_live_btn,
monitor_alerts_sidebar,
],
queue=False,
)
live_mic_input.stream(
fn=process_live_audio_stream,
inputs=[
live_mic_input,
live_audio_buffer_state,
live_separated_buffers_state,
live_patient_managers_state,
live_current_timestamps_state,
live_patient_names_state,
],
outputs=[live_audio_buffer_state, live_separated_buffers_state, live_patient_managers_state, live_current_timestamps_state, live_monitor_table, live_monitor_status, live_out_audio_1, live_out_audio_2, live_out_audio_3, live_patient_names_state, monitor_alerts_sidebar],
concurrency_limit=5,
)
stop_live_btn.click(
fn=stop_live_monitoring_session,
inputs=[],
outputs=[live_audio_buffer_state, live_separated_buffers_state, live_patient_managers_state, live_current_timestamps_state, live_monitor_table, live_monitor_status, live_mic_input, start_live_btn, live_out_audio_1, live_out_audio_2, live_out_audio_3, monitor_alerts_sidebar],
queue=False,
)
return demo
app = create_ui()
if __name__ == "__main__":
host = "0.0.0.0"
app.launch(
share=False,
server_name=host,
server_port=int(os.environ.get("PORT", 7860)),
theme=gr.themes.Soft(),
css=css_styles,
allowed_paths=[str(RESULTS_DIR.resolve())],
) |