Upload 2 files
Browse files- app.py +50 -0
- requirements.txt +11 -0
app.py
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import time
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import gradio as gr
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from PIL import Image
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from transformers import AutoTokenizer, AutoProcessor, AutoModelForCausalLM
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model_id = "Qwen/Qwen-VL-Chat"
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# 自动加载模型到合适设备(防止 OOM)
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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trust_remote_code=True,
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device_map="auto" # 自动拆分到 GPU/CPU
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).eval()
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def analyze_posture(image: Image):
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if image is None:
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return "❗ Please upload a clear photo showing your sitting posture.请上传一张坐姿清晰的照片。"
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if image.size[0] < 300 or image.size[1] < 300:
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return "⚠️ 图像分辨率过低,建议上传更清晰的坐姿照片(宽高 > 300px)"
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question = "这个人坐姿是否良好?是否驼背?用简洁中文回答,再用英文总结。"
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prompt = f"<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n"
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start = time.time()
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try:
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inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512)
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result = tokenizer.decode(outputs[0], skip_special_tokens=True)
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final = result.split("<|im_start|>assistant\n")[-1].strip()
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end = time.time()
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return f"⏱️ 回答时间:{round(end - start, 2)} 秒\n\n{final}"
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except Exception as e:
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return f"❌ 出现错误:{str(e)}\n\n💡 建议:确认图片清晰,或稍后重试。"
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# UI 设置
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demo = gr.Interface(
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fn=analyze_posture,
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inputs=gr.Image(type="pil", label="上传你的坐姿照片"),
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outputs=gr.Textbox(label="Analysis Result (Chinese + English Reminder) 分析结果(中文+英文提醒)"),
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title="🪑 Posture Monitoring Demo 坐姿监测Demo",
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description="Upload a photo to detect whether your sitting posture is good or if you have a hunchback issue. Automatically generates reminders in both Chinese and English (powered by Qwen-VL).上传照片,识别你是否坐姿良好或有驼背问题,自动生成中英文提醒(由 Qwen-VL 支持)",
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theme="soft",
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allow_flagging="never"
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)
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if __name__ == "__main__":
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demo.launch()
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requirements.txt
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transformers>=4.36.2
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torch
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gradio
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einops
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tiktoken
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torchvision
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optimum
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auto-gptq
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opencv-python
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pyttsx3
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transformers_stream_generator
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