Delete app.py

app.py

@@ -1,324 +0,0 @@
-import os
-import requests
-import json
-import time
-import threading
-import uuid
-import base64
-from pathlib import Path
-from dotenv import load_dotenv
-import gradio as gr
-import random
-import torch
-from PIL import Image, ImageDraw, ImageFont
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from functools import lru_cache
-
-load_dotenv()
-
-MODEL_URL = "TostAI/nsfw-text-detection-large"
-CLASS_NAMES = {
-    0: "✅ SAFE",
-    1: "⚠️ QUESTIONABLE",
-    2: "🚫 UNSAFE"
-}
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_URL)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_URL)
-
-class SessionManager:
-    _instances = {}
-    _lock = threading.Lock()
-
-    @classmethod
-    def get_session(cls, session_id):
-        with cls._lock:
-            if session_id not in cls._instances:
-                cls._instances[session_id] = {
-                    'count': 0,
-                    'history': [],
-                    'last_active': time.time()
-                }
-            return cls._instances[session_id]
-
-    @classmethod
-    def cleanup_sessions(cls):
-        with cls._lock:
-            now = time.time()
-            expired = [k for k, v in cls._instances.items() if now - v['last_active'] > 3600]
-            for k in expired:
-                del cls._instances[k]
-
-class RateLimiter:
-    def __init__(self):
-        self.clients = {}
-        self.lock = threading.Lock()
-
-    def check(self, client_id):
-        with self.lock:
-            now = time.time()
-            if client_id not in self.clients:
-                self.clients[client_id] = {'count': 1, 'reset': now + 3600}
-                return True
-                
-            if now > self.clients[client_id]['reset']:
-                self.clients[client_id] = {'count': 1, 'reset': now + 3600}
-                return True
-                
-            if self.clients[client_id]['count'] >= 20:
-                return False
-                
-            self.clients[client_id]['count'] += 1
-            return True
-
-session_manager = SessionManager()
-rate_limiter = RateLimiter()
-
-def image_to_base64(file_path):
-    try:
-        with open(file_path, "rb") as f:
-            img_data = f.read()
-            if len(img_data) == 0:
-                raise ValueError("空文件")
-            
-            encoded = base64.urlsafe_b64encode(img_data)
-            missing_padding = len(encoded) % 4
-            if missing_padding:
-                encoded += b'=' * (4 - missing_padding)
-                
-            ext = Path(file_path).suffix.lower()[1:]
-            mime_map = {'jpg':'jpeg','jpeg':'jpeg','png':'png','webp':'webp','gif':'gif'}
-            mime = mime_map.get(ext, 'jpeg')
-            return f"data:image/{mime};base64,{encoded.decode()}"
-    except Exception as e:
-        raise ValueError(f"Base64 Error: {str(e)}")
-
-def create_error_image(message):
-    img = Image.new("RGB", (832, 480), "#ffdddd")
-    try:
-        font = ImageFont.truetype("arial.ttf", 24)
-    except:
-        font = ImageFont.load_default()
-        
-    draw = ImageDraw.Draw(img)
-    text = f"Error: {message[:60]}..." if len(message) > 60 else message
-    draw.text((50, 200), text, fill="#ff0000", font=font)
-    img.save("error.jpg")
-    return "error.jpg"
-
-@lru_cache(maxsize=100)
-def classify_prompt(prompt):
-    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
-    with torch.no_grad():
-        outputs = model(**inputs)
-    return torch.argmax(outputs.logits).item()
-
-def generate_video(
-    image_files,
-    prompt,
-    duration,
-    enable_safety,
-    flow_shift,
-    guidance,
-    negative_prompt,
-    steps,
-    seed,
-    size,
-    session_id
-):
-
-    if len(image_files) != 2:
-        error_img = create_error_image("upload 2 images")
-        yield "❌ error: upload 2 images", error_img
-        return
-
-    safety_level = classify_prompt(prompt)
-    if safety_level != 0:
-        error_img = create_error_image(CLASS_NAMES[safety_level])
-        yield f"❌ Blocked: {CLASS_NAMES[safety_level]}", error_img
-        return
-
-    if not rate_limiter.check(session_id):
-        error_img = create_error_image("Hourly limit exceeded (20 requests)")
-        yield "❌ 请求过于频繁，请稍后再试", error_img
-        return
-
-    session = session_manager.get_session(session_id)
-    session['last_active'] = time.time()
-    session['count'] += 1
-
-    try:
-        api_key = os.getenv("WAVESPEED_API_KEY")
-        if not api_key:
-            raise ValueError("API key missing")
-
-        base64_images = [image_to_base64(img) for img in image_files]
-        
-        headers = {
-            "Authorization": f"Bearer {api_key}", 
-            "Content-Type": "application/json"
-        }
-        
-        payload = {
-            "seed": seed if seed != -1 else random.randint(0, 999999),
-            "size": size.replace(" ", ""),
-            "images": base64_images,
-            "prompt": prompt,
-            "flow_shift": flow_shift,
-            "context_scale": 1,
-            "guidance_scale": guidance,
-            "negative_prompt": negative_prompt,
-            "num_inference_steps": steps,
-            "enable_safety_checker": enable_safety,
-            "model_id": "wavespeed-ai/wan-2.1-14b-vace"
-        }
-
-        response = requests.post(
-            "https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.1-14b-vace",
-            headers=headers,
-            json=payload
-        )
-        
-        if response.status_code != 200:
-            raise Exception(f"API Error {response.status_code}: {response.text}")
-            
-        requestId = response.json()["data"]["id"]
-        yield f"✅ 任务已提交 (ID: {requestId})", None
-        
-    except Exception as e:
-        error_img = create_error_image(str(e))
-        yield f"❌ 提交失败: {str(e)}", error_img
-        return
-
-    result_url = f"https://api.wavespeed.ai/api/v3/predictions/{requestId}/result"
-    start_time = time.time()
-    
-    while True:
-        time.sleep(1)
-        try:
-            resp = requests.get(result_url, headers=headers)
-            if resp.status_code != 200:
-                raise Exception(f"状态查询失败: {resp.text}")
-                
-            data = resp.json()["data"]
-            status = data["status"]
-            
-            if status == "completed":
-                elapsed = time.time() - start_time
-                video_url = data["outputs"][0]
-                session["history"].append(video_url)
-                yield f"🎉 生成成功! 耗时 {elapsed:.1f}s", video_url
-                return
-                
-            elif status == "failed":
-                raise Exception(data.get("error", "Unknown error"))
-                
-            else:
-                yield f"⏳ 当前状态: {status.capitalize()}...", None
-                
-        except Exception as e:
-            error_img = create_error_image(str(e))
-            yield f"❌ 生成失败: {str(e)}", error_img
-            return
-
-def cleanup_task():
-    while True:
-        session_manager.cleanup_sessions()
-        time.sleep(3600)
-
-with gr.Blocks(
-    theme=gr.themes.Soft(),
-    css="""
-    .video-preview { max-width: 600px !important; }
-    .status-box { padding: 10px; border-radius: 5px; margin: 5px; }
-    .safe { background: #e8f5e9; border: 1px solid #a5d6a7; }
-    .warning { background: #fff3e0; border: 1px solid #ffcc80; }
-    .error { background: #ffebee; border: 1px solid #ef9a9a; }
-    """
-) as app:
-    
-    session_id = gr.State(str(uuid.uuid4()))
-    
-    gr.Markdown("# 🌊 Wan-2.1-14B-VACE")
-    gr.Markdown("""
-    VACE is an all-in-one model designed for video creation and editing. It encompasses various tasks, including reference-to-video generation (R2V), video-to-video editing (V2V), and masked video-to-video editing (MV2V), allowing users to compose these tasks freely. This functionality enables users to explore diverse possibilities and streamlines their workflows effectively, offering a range of capabilities, such as Move-Anything, Swap-Anything, Reference-Anything, Expand-Anything, Animate-Anything, and more.
-    """)
-    gr.Markdown("""
-        [WaveSpeedAI](https://wavespeed.ai/) 提供先进的AI视频生成加速技术
-        """)
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            img_input = gr.File(
-                file_count="multiple",
-                file_types=["image"],
-                label="upload 2 images"
-            )
-            prompt = gr.Textbox(label="prompt", lines=3, placeholder="请输入描述...")
-            negative_prompt = gr.Textbox(label="negative_prompt", lines=2)
-            
-            with gr.Row():
-                size = gr.Dropdown(
-                    ["480*832", "832*480"], 
-                    value="480*832",
-                    label="resolution"
-                )
-                steps = gr.Slider(1, 50, value=30, label="推理步数")
-            with gr.Row():
-                duration = gr.Slider(1, 10, value=5, step=1, label="视频时长(秒)")
-                guidance = gr.Slider(1, 20, value=7, label="引导系数")
-            with gr.Row():
-                seed = gr.Number(-1, label="随机种子")
-                random_seed_btn = gr.Button("随机种子🎲", variant="secondary")
-            with gr.Row():
-                enable_safety = gr.Checkbox(label="🔒 安全检测", value=True)
-                flow_shift = gr.Slider(1, 50, value=16, label="运动幅度")
-                
-        with gr.Column(scale=1):
-            video_output = gr.Video(label="生成结果", format="mp4")
-            status_output = gr.Textbox(label="系统状态", interactive=False, lines=4)
-            generate_btn = gr.Button("开始生成", variant="primary")
-                
-    gr.Examples(
-        examples=[[
-            "The elegant lady carefully selects bags in the boutique...",
-            [
-                "https://d2g64w682n9w0w.cloudfront.net/media/ec44bbf6abac4c25998dd2c4af1a46a7/images/1747413751234102420_md9ywspl.png",
-                "https://d2g64w682n9w0w.cloudfront.net/media/ec44bbf6abac4c25998dd2c4af1a46a7/images/1747413586520964413_7bkgc9ol.png"
-            ]
-        ]],
-        inputs=[prompt, img_input],
-        label="示例输入",
-        examples_per_page=3
-    )
-
-    random_seed_btn.click(
-        fn=lambda: random.randint(0, 999999),
-        outputs=seed
-    )
-    
-    generate_btn.click(
-        generate_video,
-        inputs=[
-            img_input,
-            prompt,
-            duration,
-            enable_safety,
-            flow_shift,
-            guidance,
-            negative_prompt,
-            steps,
-            seed,
-            size,
-            session_id 
-        ],
-        outputs=[status_output, video_output]
-    )
-
-if __name__ == "__main__":
-    threading.Thread(target=cleanup_task, daemon=True).start()
-    app.queue(max_size=4).launch(
-        server_name="0.0.0.0",
-        max_threads=16,
-        share=False
-    )