Delete app.py

app.py

@@ -1,43 +0,0 @@
-import gradio as gr
-import mediapipe as mp
-import cv2
-import numpy as np
-from PIL import Image
-import torch
-from transformers import AutoProcessor, AutoTokenizer, AutoModelForCausalLM
-from posture_utils import analyze_posture_by_keypoints
-
-model_id = "Qwen/Qwen2-VL-7B"
-tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
-processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, device_map="auto").eval()
-
-mp_pose = mp.solutions.pose
-
-def process(image: Image):
-    np_image = np.array(image)
-    with mp_pose.Pose(static_image_mode=True) as pose:
-        results = pose.process(cv2.cvtColor(np_image, cv2.COLOR_RGB2BGR))
-
-        if not results.pose_landmarks:
-            return "❗ 无法检测到人体，请上传包含上半身的清晰坐姿照片。"
-
-        posture_analysis = analyze_posture_by_keypoints(results.pose_landmarks)
-        prompt = f"<|im_start|>user\n请根据以下坐姿描述生成中英文提醒：\n{posture_analysis}\n<|im_end|>\n<|im_start|>assistant\n"
-        inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device)
-        output = model.generate(**inputs, max_new_tokens=512)
-        result = tokenizer.decode(output[0], skip_special_tokens=True)
-        return result.split("<|im_start|>assistant\n")[-1].strip()
-
-demo = gr.Interface(
-    fn=process,
-    inputs=gr.Image(type="pil", label="上传你的坐姿照片"),
-    outputs=gr.Textbox(label="中英文坐姿分析结果"),
-    title="🪑 坐姿监测融合助手",
-    description="上传坐姿图像，先通过 Mediapipe 判断是否驼背、低头、含胸，再交由 Qwen-VL 生成提醒语。",
-    theme="soft",
-    allow_flagging="never"
-)
-
-if __name__ == "__main__":
-    demo.launch()