228 lines
No EOL
8.4 KiB
Python
228 lines
No EOL
8.4 KiB
Python
import os
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import multiprocessing
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import threading
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import subprocess
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from flask import Flask, request, render_template, jsonify, redirect, url_for
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from timelapse import process_faces, ProcessConfig, validate_immich_connection
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app = Flask(__name__)
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# Critical parameters provided via environment variables
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API_KEY = os.environ.get("IMMICH_API_KEY", "")
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BASE_URL = os.environ.get("IMMICH_BASE_URL", "")
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OUTPUT_FOLDER = "output"
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# Model paths
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FACE_DETECT_MODEL = "mmod_human_face_detector.dat"
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LANDMARK_MODEL = "shape_predictor_68_face_landmarks.dat"
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LEFT_EYE_POS = (0.35, 0.45)
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AVAILABLE_CORES = multiprocessing.cpu_count()
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# Global progress dictionary – only one job at a time is assumed here
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progress_info = {"completed": 0, "total": 0, "status": "idle"}
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# Global processing thread reference
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processing_thread = None
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# Global flag to signal cancellation
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cancel_requested = False
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def update_progress(current, total):
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"""
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Updates the global progress dictionary.
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Args:
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current (int): Number of completed tasks.
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total (int): Total number of tasks.
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"""
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progress_info["completed"] = current
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progress_info["total"] = total
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progress_info["status"] = "running" if current < total else "done"
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def background_process(person_id, padding_percent, resize_size, face_resolution_threshold, pose_threshold, max_workers,
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date_from, date_to, compile_video, framerate):
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"""
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Background process that creates a configuration object and calls process_faces.
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Args:
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person_id (str): The target person ID.
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padding_percent (float): Padding percentage for face cropping.
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resize_size (int): Desired width and height for the aligned face image.
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face_resolution_threshold (int): Minimum required face resolution.
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pose_threshold (float): Maximum allowed head pose deviation.
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max_workers (int): Number of concurrent worker processes.
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date_from (str): Start date for asset filtering.
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date_to (str): End date for asset filtering.
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compile_video (bool): Whether to compile the images into a video.
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framerate (int): Frames per second for the output video.
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"""
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global cancel_requested
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try:
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progress_info["status"] = "running"
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# Build the configuration object for processing
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config = ProcessConfig(
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api_key=API_KEY,
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base_url=BASE_URL,
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person_id=person_id,
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output_folder=OUTPUT_FOLDER,
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padding_percent=padding_percent,
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resize_width=resize_size,
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resize_height=resize_size,
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min_face_width=face_resolution_threshold,
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min_face_height=face_resolution_threshold,
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pose_threshold=pose_threshold,
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desired_left_eye=LEFT_EYE_POS,
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face_detect_model_path=FACE_DETECT_MODEL,
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landmark_model_path=LANDMARK_MODEL
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)
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# Pass the cancel flag to the process_faces function
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processed_files = process_faces(config, max_workers=max_workers, progress_callback=update_progress,
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date_from=date_from, date_to=date_to, cancel_flag=lambda: cancel_requested)
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# Check if processing was cancelled
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if cancel_requested:
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progress_info["status"] = "cancelled"
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cancel_requested = False
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return
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# Compile video if requested and there are processed files
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if compile_video and processed_files:
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output_video = os.path.join(OUTPUT_FOLDER, "timelapse.mp4")
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ffmpeg_command = [
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"ffmpeg", "-y",
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"-framerate", str(framerate),
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"-pattern_type", "glob",
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"-i", os.path.join(OUTPUT_FOLDER, "*.jpg"),
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"-c:v", "libx264",
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"-pix_fmt", "yuv420p",
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output_video
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]
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try:
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subprocess.run(ffmpeg_command, check=True)
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progress_info["video_path"] = output_video
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progress_info["status"] = "video_done"
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except subprocess.CalledProcessError as e:
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progress_info["status"] = f"Video compilation failed: {e}"
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else:
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progress_info["status"] = "done"
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except Exception as e:
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progress_info["status"] = f"error: {e}"
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def check_output_folder():
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"""
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Checks if the output folder is empty.
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Returns:
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tuple: (is_empty, file_count) - Boolean indicating if folder is empty and number of files
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"""
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if not os.path.exists(OUTPUT_FOLDER):
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os.makedirs(OUTPUT_FOLDER, exist_ok=True)
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return True, 0
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files = [f for f in os.listdir(OUTPUT_FOLDER) if os.path.isfile(os.path.join(OUTPUT_FOLDER, f))]
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return len(files) == 0, len(files)
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@app.route("/progress")
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def progress():
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"""
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Endpoint to return current progress as JSON.
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"""
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return jsonify(progress_info)
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@app.route("/check-connection")
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def check_connection():
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"""
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Endpoint to check the Immich server connection.
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"""
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is_valid, message = validate_immich_connection(API_KEY, BASE_URL)
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return jsonify({"valid": is_valid, "message": message})
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@app.route("/cancel", methods=["POST"])
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def cancel():
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"""
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Endpoint to cancel the current processing job.
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"""
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global processing_thread, cancel_requested
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# Set the cancel flag
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cancel_requested = True
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if processing_thread and processing_thread.is_alive():
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progress_info["status"] = "cancelled"
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return jsonify({"success": True, "message": "Processing cancelled."})
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else:
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cancel_requested = False
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return jsonify({"success": False, "message": "No active processing to cancel."})
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@app.route("/", methods=["GET", "POST"])
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def index():
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"""
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Index route that displays the form and starts processing in a background thread on POST.
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"""
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global processing_thread, cancel_requested
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result = None
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error = None
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warning = None
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# Check if output folder is empty
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is_empty, file_count = check_output_folder()
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if not is_empty:
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warning = f"Output folder is not empty. Contains {file_count} files. New images will be added to this folder."
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# Check if Immich server connection is valid
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is_valid, message = validate_immich_connection(API_KEY, BASE_URL)
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if not is_valid and request.method == "POST":
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error = f"Immich server connection error: {message}"
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return render_template("index.html", error=error, warning=warning,
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max_workers_options=list(range(1, AVAILABLE_CORES + 1)))
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if request.method == "POST":
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try:
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# Make sure any previous cancel request is cleared
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cancel_requested = False
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person_id = request.form["person_id"]
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padding_percent = float(request.form.get("padding_percent", 30)) / 100
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resize_size = int(request.form.get("resize_size", 512))
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face_resolution_threshold = int(request.form.get("face_resolution_threshold", 128))
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pose_threshold = float(request.form.get("pose_threshold", 25))
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max_workers = int(request.form.get("max_workers", 1))
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# Date ranges are optional
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date_from = request.form.get("date_from") or None
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date_to = request.form.get("date_to") or None
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compile_video = request.form.get("compile_video") == "on"
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framerate = int(request.form.get("framerate", 24))
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# Reset progress info before starting
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progress_info["completed"] = 0
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progress_info["total"] = 0
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progress_info["status"] = "idle"
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progress_info.pop("video_path", None)
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# Start the processing in a background thread
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processing_thread = threading.Thread(
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target=background_process,
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args=(person_id, padding_percent, resize_size, face_resolution_threshold, pose_threshold, max_workers,
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date_from, date_to, compile_video, framerate)
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)
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processing_thread.start()
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result = "Processing started. Please wait and watch the progress bar below."
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except Exception as e:
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error = f"Error processing request: {e}"
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return render_template("index.html", result=result, error=error, warning=warning,
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max_workers_options=list(range(1, AVAILABLE_CORES + 1)))
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if __name__ == "__main__":
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app.run(host="0.0.0.0", port=5000) |