752 lines
30 KiB
Python
752 lines
30 KiB
Python
import argparse
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import os
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import requests
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import base64
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import time
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import json
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from pathlib import Path
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import tempfile
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import gradio as gr
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from fastvideo.configs.sample.base import SamplingParam
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MODEL_PATH_MAPPING = {
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"FastWan2.1-T2V-1.3B": "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
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"FastWan2.2-TI2V-5B-FullAttn": "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
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"CausalWan2.2-I2V-A14B-Preview": "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
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}
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class RayServeClient:
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def __init__(self, backend_url: str):
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self.backend_url = backend_url
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self.session = requests.Session()
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def check_health(self) -> bool:
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try:
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response = self.session.get(f"{self.backend_url}/health", timeout=5)
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return response.status_code == 200
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except requests.exceptions.RequestException:
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return False
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def generate_video(self, request_data: dict) -> dict:
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start_time = time.time()
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try:
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headers = {"Content-Type": "application/json"}
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response = self.session.post(
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f"{self.backend_url}/generate_video",
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json=request_data,
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headers=headers,
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timeout=900 # 15 minutes timeout for longer video generation
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)
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round_trip_time = time.time() - start_time
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if response.status_code == 200:
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result = response.json()
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backend_total = result.get("total_time", 0)
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network_time = round_trip_time - backend_total
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result["network_time"] = network_time
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return result
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else:
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return {"success": False, "error_message": f"HTTP {response.status_code}: {response.text}"}
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except requests.exceptions.RequestException as e:
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return {"success": False, "error_message": f"Request failed: {str(e)}"}
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def save_video_from_base64(video_data: str, output_dir: str, prompt: str) -> str:
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if not video_data:
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return None
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try:
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if video_data.startswith('data:video/'):
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video_data = video_data.split(',')[1]
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video_bytes = base64.b64decode(video_data)
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safe_prompt = prompt[:50].replace(' ', '_').replace('/', '_').replace('\\', '_')
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video_filename = f"{safe_prompt}.mp4"
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video_path = os.path.join(output_dir, video_filename)
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os.makedirs(output_dir, exist_ok=True)
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with open(video_path, 'wb') as f:
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f.write(video_bytes)
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return video_path
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except Exception as e:
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print(f"Failed to save video: {e}")
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return None
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def encode_image_to_base64(image_path: str) -> str:
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"""Encode an image file to base64 string."""
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if not image_path or not os.path.exists(image_path):
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return None
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try:
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with open(image_path, 'rb') as f:
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image_bytes = f.read()
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image_base64 = base64.b64encode(image_bytes).decode('utf-8')
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# Determine image type from extension
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ext = os.path.splitext(image_path)[1].lower()
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mime_types = {
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'.jpg': 'image/jpeg',
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'.jpeg': 'image/jpeg',
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'.png': 'image/png',
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'.gif': 'image/gif',
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'.webp': 'image/webp',
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}
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mime_type = mime_types.get(ext, 'image/jpeg')
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return f"data:{mime_type};base64,{image_base64}"
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except Exception as e:
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print(f"Failed to encode image: {e}")
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return None
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# def create_timing_display(inference_time, encoding_time, network_time, total_time, stage_execution_times, num_frames):
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# dit_denoising_time = f"{stage_execution_times[5]:.2f}s" if len(stage_execution_times) > 5 else "N/A"
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#
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# timing_html = f"""
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# <div style="margin: 10px 0;">
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# <h3 style="text-align: center; margin-bottom: 10px;">⏱️ Timing Breakdown</h3>
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# <div style="display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 10px;">
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# <div class="timing-card timing-card-highlight">
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# <div style="font-size: 20px;">🚀</div>
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# <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">DiT Denoising</div>
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# <div style="font-size: 16px; color: #ffa200; font-weight: bold;">{dit_denoising_time}</div>
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# </div>
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# <div class="timing-card">
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# <div style="font-size: 20px;">🧠</div>
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# <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">E2E (w. vae/text encoder)</div>
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# <div style="font-size: 16px; color: #2563eb;">{inference_time:.2f}s</div>
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# </div>
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# <div class="timing-card">
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# <div style="font-size: 20px;">🎬</div>
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# <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Video Encoding</div>
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# <div style="font-size: 16px; color: #dc2626;">{encoding_time:.2f}s</div>
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# </div>
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# <div class="timing-card">
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# <div style="font-size: 20px;">🌐</div>
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# <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Network Transfer</div>
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# <div style="font-size: 16px; color: #059669;">{network_time:.2f}s</div>
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# </div>
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# <div class="timing-card">
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# <div style="font-size: 20px;">📊</div>
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# <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Total Processing</div>
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# <div style="font-size: 18px; color: #0277bd;">{total_time:.2f}s</div>
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# </div>
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# </div>"""
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#
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# if inference_time > 0:
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# fps = num_frames / inference_time
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# timing_html += f"""
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# <div class="performance-card" style="margin-top: 15px;">
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# <span style="font-weight: bold;">Generation Speed: </span>
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# <span style="font-size: 18px; color: #6366f1; font-weight: bold;">{fps:.1f} frames/second</span>
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# </div>"""
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#
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# return timing_html + "</div>"
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def load_example_prompts():
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def contains_chinese(text):
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return any('\u4e00' <= char <= '\u9fff' for char in text)
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def load_from_file(filepath):
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prompts, labels = [], []
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try:
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with open(filepath, "r", encoding='utf-8') as f:
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for line in f:
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line = line.strip()
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if line and not contains_chinese(line):
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label = line[:100] + "..." if len(line) > 100 else line
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labels.append(label)
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prompts.append(line)
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except Exception as e:
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print(f"Warning: Could not read {filepath}: {e}")
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return prompts, labels
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# Load prompts from prompts.txt
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examples, example_labels = load_from_file("examples/inference/gradio/serving/prompts.txt")
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if not examples:
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examples = ["A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background."]
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example_labels = ["Crowded rooftop bar at night"]
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# Load image mappings from JSON file
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prompt_to_image = {}
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# Try to find the JSON file relative to project root
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possible_json_paths = [
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Path("assets/prompts/mixkit_i2v.jsonl"),
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Path(__file__).resolve().parents[4] / "assets" / "prompts" /
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"mixkit_i2v.jsonl",
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]
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json_path = None
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for path in possible_json_paths:
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if path.exists():
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json_path = path
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break
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if json_path and json_path.exists():
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try:
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with open(json_path, "r", encoding='utf-8') as f:
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data = json.load(f)
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# Resolve paths relative to repository root.
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project_root = Path(__file__).resolve().parents[4]
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for item in data:
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prompt_text = item.get("prompt", "").strip()
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image_path = item.get("image_path", "")
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if prompt_text and image_path:
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# Resolve image path relative to project root
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full_image_path = project_root / image_path
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if full_image_path.exists():
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prompt_to_image[prompt_text] = str(full_image_path.absolute())
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except Exception as e:
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print(f"Warning: Could not load image mappings from {json_path}: {e}")
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# Create image paths list matching the prompts
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example_images = []
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for prompt in examples:
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# Try exact match first
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image_path = prompt_to_image.get(prompt)
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if not image_path:
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# Try fuzzy match (case-insensitive, whitespace normalized)
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normalized_prompt = " ".join(prompt.split())
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for json_prompt, img_path in prompt_to_image.items():
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normalized_json = " ".join(json_prompt.split())
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if normalized_prompt.lower() == normalized_json.lower():
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image_path = img_path
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break
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example_images.append(image_path if image_path and os.path.exists(image_path) else None)
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return examples, example_labels, example_images
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def create_gradio_interface(backend_url: str, default_params: dict[str, SamplingParam]):
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client = RayServeClient(backend_url)
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def is_i2v_model(model_name: str) -> bool:
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"""Check if the model is an I2V model."""
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return "I2V" in model_name
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def generate_video(
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prompt, negative_prompt, use_negative_prompt, guidance_scale,
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num_frames, height, width, model_selection, input_image, progress
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):
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# Use default seed value (randomize_seed disabled)
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seed = 1000
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randomize_seed = False
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if not client.check_health():
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return None, f"Backend is not available. Please check if Ray Serve is running at {backend_url}", ""
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# Check if I2V model requires an image
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if is_i2v_model(model_selection) and not input_image:
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return None, "I2V models require an input image. Please upload an image.", ""
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# Validate dimensions
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max_pixels = 720 * 1280
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if height * width > max_pixels:
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return None, f"Video dimensions too large. Maximum: 720x1280 pixels", ""
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if progress:
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progress(0.1, desc="Checking backend health...")
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# Encode image if provided
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image_data = None
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if input_image:
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if progress:
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progress(0.2, desc="Encoding input image...")
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image_data = encode_image_to_base64(input_image)
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if not image_data:
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return None, "Failed to encode input image", ""
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request_data = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"use_negative_prompt": use_negative_prompt,
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"seed": seed,
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"guidance_scale": guidance_scale,
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"num_frames": num_frames,
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"height": height,
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"width": width,
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"randomize_seed": randomize_seed,
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"return_frames": False,
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"image_data": image_data,
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"model_path": MODEL_PATH_MAPPING.get(model_selection, "FastVideo/FastWan2.1-T2V-1.3B-Diffusers")
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}
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if progress:
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progress(0.4, desc="Generating video...")
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response = client.generate_video(request_data)
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if progress:
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progress(0.8, desc="Processing response...")
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if response.get("success", False):
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video_data = response.get("video_data", "")
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used_seed = response.get("seed", seed)
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# inference_time = response.get("inference_time", 0.0)
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# encoding_time = response.get("encoding_time", 0.0)
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# total_time = response.get("total_time", 0.0)
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# network_time = response.get("network_time", 0.0)
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# stage_execution_times = response.get("stage_execution_times", [])
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# timing_details = create_timing_display(
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# inference_time, encoding_time, network_time, total_time,
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# stage_execution_times, num_frames
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# )
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if video_data:
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if progress:
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progress(0.9, desc="Saving video...")
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video_path = save_video_from_base64(video_data, "outputs", prompt)
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if progress:
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progress(1.0, desc="Generation complete!")
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if video_path and os.path.exists(video_path):
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return video_path, used_seed, ""
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else:
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return None, "Failed to save video", ""
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else:
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return None, "No video data received from backend", ""
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else:
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error_msg = response.get("error_message", "Unknown error occurred")
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return None, f"Generation failed: {error_msg}", ""
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examples, example_labels, example_images = load_example_prompts()
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theme = gr.themes.Base().set(
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button_primary_background_fill="#2563eb",
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button_primary_background_fill_hover="#1d4ed8",
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button_primary_text_color="white",
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slider_color="#2563eb",
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checkbox_background_color_selected="#2563eb",
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)
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def get_default_values(model_name):
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# model_path = MODEL_PATH_MAPPING.get(model_name)
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# if model_path and model_path in default_params:
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# params = default_params[model_path]
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# return {
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# 'height': params.height,
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# 'width': params.width,
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# 'num_frames': params.num_frames,
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# 'guidance_scale': params.guidance_scale,
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# }
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return {
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'height': 480,
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'width': 832,
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'num_frames': 73,
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}
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# Get available models based on what's loaded
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available_models = []
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for model_name, model_path in MODEL_PATH_MAPPING.items():
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if model_path in default_params:
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available_models.append(model_name)
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# Select first available model as default
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default_model = available_models[0] if available_models else "FastWan2.1-T2V-1.3B"
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initial_values = get_default_values(default_model)
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initial_show_image = is_i2v_model(default_model)
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with gr.Blocks(title="CausalWan", theme=theme) as demo:
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gr.Image("assets/logos/logo.svg", show_label=False, container=False, height=80)
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gr.HTML("""
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<div style="text-align: center; margin-bottom: 10px;">
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<p style="font-size: 18px;"> Make Video Generation Go Blurrrrrrr </p>
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<p style="font-size: 18px;"> <a href="https://github.com/hao-ai-lab/FastVideo/tree/main" target="_blank">Code</a> | <a href="https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/" target="_blank">Blog</a> | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a> </p>
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</div>
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""")
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with gr.Accordion("🎥 What Is FastVideo?", open=False):
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gr.HTML("""
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<div style="padding: 20px; line-height: 1.6;">
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<p style="font-size: 16px; margin-bottom: 15px;">
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FastVideo is an inference and post-training framework for diffusion models. It features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
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</p>
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</div>
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""")
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with gr.Row():
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model_selection = gr.Dropdown(
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choices=available_models,
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value=default_model,
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label="Select Model",
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interactive=True
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)
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with gr.Row():
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example_dropdown = gr.Dropdown(
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choices=example_labels,
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label="Example Prompts",
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value=None,
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interactive=True,
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allow_custom_value=False
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)
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with gr.Row():
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with gr.Column(scale=6):
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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max_lines=3,
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placeholder="Describe your scene...",
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container=False,
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lines=3,
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autofocus=True,
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)
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with gr.Column(scale=1, min_width=120, elem_classes="center-button"):
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run_button = gr.Button("Run", variant="primary", size="lg")
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with gr.Row():
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with gr.Column():
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error_output = gr.Text(label="Error", visible=False)
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# timing_display = gr.Markdown(label="Timing Breakdown", visible=False)
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with gr.Row(equal_height=False):
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with gr.Column(scale=1):
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with gr.Tabs():
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with gr.Tab("Input Image", visible=initial_show_image) as image_tab:
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gr.Markdown("**Please make sure you upload a 480x832 image**")
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input_image = gr.Image(
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label="",
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type="filepath",
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height=400,
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)
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with gr.Tab("Advanced Options"):
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with gr.Group():
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with gr.Row():
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height = gr.Number(
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label="Height",
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value=initial_values['height'],
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interactive=False,
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container=True
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)
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width = gr.Number(
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label="Width",
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value=initial_values['width'],
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interactive=False,
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container=True
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)
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with gr.Row():
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num_frames = gr.Number(
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label="Number of Frames",
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value=initial_values['num_frames'],
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interactive=False,
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container=True
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)
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guidance_scale = gr.Number(
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label="Guidance Scale",
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value=1.0,
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interactive=False,
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container=True
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)
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with gr.Row():
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use_negative_prompt = gr.Checkbox(
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label="Use negative prompt", value=False)
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=3,
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lines=3,
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placeholder="Enter a negative prompt",
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visible=False,
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)
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# randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
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seed_output = gr.Number(label="Used Seed", value=1000)
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with gr.Column(scale=1):
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result = gr.Video(
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label="Generated Video",
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show_label=True,
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height=500,
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container=True,
|
|
autoplay=True,
|
|
)
|
|
|
|
gr.HTML("""
|
|
<style>
|
|
.center-button {
|
|
display: flex !important;
|
|
justify-content: center !important;
|
|
height: 100% !important;
|
|
padding-top: 1.4em !important;
|
|
}
|
|
|
|
.gradio-container {
|
|
max-width: 1400px !important;
|
|
margin: 0 auto !important;
|
|
}
|
|
|
|
.gr-number input[readonly] {
|
|
background-color: var(--background-fill-secondary) !important;
|
|
border: 1px solid var(--border-color-primary) !important;
|
|
color: var(--body-text-color-subdued) !important;
|
|
cursor: default !important;
|
|
text-align: center !important;
|
|
font-weight: 500 !important;
|
|
}
|
|
</style>
|
|
""")
|
|
|
|
def on_example_select(example_label):
|
|
if example_label and example_label in example_labels:
|
|
index = example_labels.index(example_label)
|
|
selected_prompt = examples[index]
|
|
selected_image = example_images[index] if index < len(example_images) else None
|
|
return selected_prompt, selected_image
|
|
return "", None
|
|
|
|
example_dropdown.change(
|
|
fn=on_example_select,
|
|
inputs=example_dropdown,
|
|
outputs=[prompt, input_image],
|
|
)
|
|
|
|
gr.HTML("""
|
|
<div style="text-align: center; margin-top: 10px; margin-bottom: 15px;">
|
|
<p style="font-size: 16px; margin: 0;">The compute for this demo is generously provided by <a href="https://www.gmicloud.ai/" target="_blank">GMI Cloud</a>. Note that this demo is meant as a preview of our distilled I2V model. Outside of few-step distillation, we have not yet fully optimized it for speed. Stay tuned for updates!</p>
|
|
</div>
|
|
""")
|
|
|
|
use_negative_prompt.change(
|
|
fn=lambda x: gr.update(visible=x),
|
|
inputs=use_negative_prompt,
|
|
outputs=negative_prompt,
|
|
)
|
|
|
|
def on_model_selection_change(selected_model):
|
|
if not selected_model:
|
|
selected_model = "FastWan2.1-T2V-1.3B"
|
|
|
|
model_path = MODEL_PATH_MAPPING.get(selected_model)
|
|
show_image_input = is_i2v_model(selected_model)
|
|
|
|
if model_path and model_path in default_params:
|
|
params = default_params[model_path]
|
|
return (
|
|
gr.update(value=params.height),
|
|
gr.update(value=params.width),
|
|
gr.update(value=params.num_frames),
|
|
gr.update(value=params.guidance_scale),
|
|
gr.update(visible=show_image_input),
|
|
)
|
|
|
|
return (
|
|
gr.update(value=448),
|
|
gr.update(value=832),
|
|
gr.update(value=20),
|
|
gr.update(value=3.0),
|
|
gr.update(visible=show_image_input),
|
|
)
|
|
|
|
model_selection.change(
|
|
fn=on_model_selection_change,
|
|
inputs=model_selection,
|
|
outputs=[height, width, num_frames, guidance_scale, image_tab],
|
|
)
|
|
|
|
def handle_generation(*args, progress=None, request: gr.Request = None):
|
|
model_selection, prompt, negative_prompt, use_negative_prompt, guidance_scale, num_frames, height, width, input_image = args
|
|
|
|
result_path, seed_or_error, _ = generate_video(
|
|
prompt, negative_prompt, use_negative_prompt, guidance_scale,
|
|
num_frames, height, width, model_selection, input_image, progress
|
|
)
|
|
|
|
if result_path and os.path.exists(result_path):
|
|
return (
|
|
result_path,
|
|
seed_or_error,
|
|
gr.update(visible=False),
|
|
)
|
|
else:
|
|
return (
|
|
None,
|
|
seed_or_error,
|
|
gr.update(visible=True, value=seed_or_error),
|
|
)
|
|
|
|
run_button.click(
|
|
fn=handle_generation,
|
|
inputs=[
|
|
model_selection,
|
|
prompt,
|
|
negative_prompt,
|
|
use_negative_prompt,
|
|
guidance_scale,
|
|
num_frames,
|
|
height,
|
|
width,
|
|
# randomize_seed,
|
|
input_image,
|
|
],
|
|
outputs=[result, seed_output, error_output], # timing_display removed
|
|
concurrency_limit=20,
|
|
)
|
|
|
|
return demo
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="FastVideo Gradio Frontend")
|
|
parser.add_argument("--backend_url", type=str, default="http://localhost:8000",
|
|
help="URL of the Ray Serve backend")
|
|
parser.add_argument("--t2v_model_paths", type=str,
|
|
default="",
|
|
help="Comma separated list of paths to the T2V model(s)")
|
|
parser.add_argument("--i2v_model_paths", type=str,
|
|
default="FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
|
|
help="Comma separated list of paths to the I2V model(s)")
|
|
parser.add_argument("--host", type=str, default="0.0.0.0",
|
|
help="Host to bind to")
|
|
parser.add_argument("--port", type=int, default=7860,
|
|
help="Port to bind to")
|
|
|
|
args = parser.parse_args()
|
|
|
|
default_params = {}
|
|
|
|
# Load T2V model params
|
|
t2v_paths = [p.strip() for p in args.t2v_model_paths.split(",") if p.strip()]
|
|
for model_path in t2v_paths:
|
|
default_params[model_path] = SamplingParam.from_pretrained(model_path)
|
|
|
|
# Load I2V model params
|
|
i2v_paths = [p.strip() for p in args.i2v_model_paths.split(",") if p.strip()]
|
|
for model_path in i2v_paths:
|
|
default_params[model_path] = SamplingParam.from_pretrained(model_path)
|
|
|
|
demo = create_gradio_interface(args.backend_url, default_params)
|
|
|
|
print(f"Starting Gradio frontend at http://{args.host}:{args.port}")
|
|
print(f"Backend URL: {args.backend_url}")
|
|
print(f"T2V Models: {args.t2v_model_paths}")
|
|
if args.i2v_model_paths:
|
|
print(f"I2V Models: {args.i2v_model_paths}")
|
|
|
|
from fastapi import FastAPI, Request, HTTPException
|
|
from fastapi.responses import HTMLResponse, FileResponse
|
|
import uvicorn
|
|
|
|
app = FastAPI()
|
|
|
|
@app.get("/logo.svg")
|
|
def get_logo():
|
|
return FileResponse(
|
|
"assets/logos/logo.svg",
|
|
media_type="image/svg+xml",
|
|
headers={
|
|
"Cache-Control": "public, max-age=3600",
|
|
"Access-Control-Allow-Origin": "*"
|
|
}
|
|
)
|
|
|
|
@app.get("/favicon.ico")
|
|
def get_favicon():
|
|
favicon_path = "assets/logos/icon_simple.svg"
|
|
|
|
if os.path.exists(favicon_path):
|
|
return FileResponse(
|
|
favicon_path,
|
|
media_type="image/svg+xml",
|
|
headers={
|
|
"Cache-Control": "public, max-age=3600",
|
|
"Access-Control-Allow-Origin": "*"
|
|
}
|
|
)
|
|
else:
|
|
raise HTTPException(status_code=404, detail="Favicon not found")
|
|
|
|
@app.get("/", response_class=HTMLResponse)
|
|
def index(request: Request):
|
|
base_url = str(request.base_url).rstrip('/')
|
|
return f"""
|
|
<!DOCTYPE html>
|
|
<html lang="en">
|
|
<head>
|
|
<meta charset="UTF-8" />
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
|
|
|
<title>CausalWan</title>
|
|
<meta name="title" content="CausalWan">
|
|
<meta name="description" content="Make video generation go blurrrrrrr">
|
|
<meta name="keywords" content="FastVideo, video generation, AI, machine learning, CausalWan">
|
|
|
|
<meta property="og:type" content="website">
|
|
<meta property="og:url" content="{base_url}/">
|
|
<meta property="og:title" content="CausalWan">
|
|
<meta property="og:description" content="Make video generation go blurrrrrrr">
|
|
<meta property="og:image" content="{base_url}/logo.svg">
|
|
<meta property="og:image:width" content="1200">
|
|
<meta property="og:image:height" content="630">
|
|
<meta property="og:site_name" content="CausalWan">
|
|
|
|
<meta property="twitter:card" content="summary_large_image">
|
|
<meta property="twitter:url" content="{base_url}/">
|
|
<meta property="twitter:title" content="CausalWan">
|
|
<meta property="twitter:description" content="Make video generation go blurrrrrrr">
|
|
<meta property="twitter:image" content="{base_url}/logo.svg">
|
|
<link rel="icon" type="image/png" sizes="32x32" href="/favicon.ico">
|
|
<link rel="icon" type="image/png" sizes="16x16" href="/favicon.ico">
|
|
<link rel="apple-touch-icon" href="/favicon.ico">
|
|
<style>
|
|
body, html {{
|
|
margin: 0;
|
|
padding: 0;
|
|
height: 100%;
|
|
overflow: hidden;
|
|
}}
|
|
iframe {{
|
|
width: 100%;
|
|
height: 100vh;
|
|
border: none;
|
|
}}
|
|
</style>
|
|
</head>
|
|
<body>
|
|
<iframe src="/gradio" width="100%" height="100%" style="border: none;"></iframe>
|
|
</body>
|
|
</html>
|
|
"""
|
|
|
|
app = gr.mount_gradio_app(
|
|
app,
|
|
demo,
|
|
path="/gradio",
|
|
allowed_paths=[
|
|
os.path.abspath("outputs"),
|
|
os.path.abspath("fastvideo-logos"),
|
|
os.path.abspath("assets/prompts"),
|
|
os.path.abspath("assets/images"),
|
|
os.path.abspath(tempfile.gettempdir()),
|
|
os.path.abspath(os.path.join(tempfile.gettempdir(), "gradio")),
|
|
]
|
|
)
|
|
|
|
uvicorn.run(app, host=args.host, port=args.port)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|