update app

app.py

@@ -0,0 +1,62 @@
+import os
+os.system("pip install opencv-python")
+os.system("pip install tensorflow")
+os.system("pip install modelscope")
+
+import gradio as gr
+import cv2
+import PIL.Image as Image
+import numpy as np
+import torch
+from PIL import ImageFont
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+import warnings
+warnings.filterwarnings("ignore")
+
+def recognize_image(img):
+    # 加载模型
+    general_recognition = pipeline(Tasks.general_recognition, model='damo/cv_resnest101_general_recognition')
+    img = np.array(img)
+    # 模型推理
+    result = general_recognition(img)
+    print(result)
+    # 绘制分数和类别信息
+    scores = result['scores']
+    labels = result['labels']
+
+    # 读取中文字体文件
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    font_scale = 1
+    font_color = (0, 0, 255)
+    thickness = 2
+
+    for i in range(len(scores)):
+        score = scores[i]
+        label = labels[i]
+        img = cv2.putText(img, f"{label}: {score:.2f}", (10, 30), font, font_scale, font_color, thickness)
+
+    img_pil = Image.fromarray(np.uint8(img))
+    return img_pil
+
+
+def download_test_image():
+    # Images
+    torch.hub.download_url_to_file(
+        'https://pailitao-image-recog.oss-cn-zhangjiakou.aliyuncs.com/mufan/img_data/maas_test_data/dog.png',
+        'dog.png')
+
+
+download_test_image()
+input_image = gr.inputs.Image(type='pil', label="输入图片")
+output_image = gr.outputs.Image(type='pil', label="输出图片")
+examples = [["dog.png"]]
+title = "万物识别-中文-通用领域 web demo"
+interface = gr.Interface(fn=recognize_image,
+                         inputs=input_image,
+                         outputs=output_image,
+                         examples=examples,
+                         title=title)
+
+interface.launch()