Create app.py

app.py

@@ -0,0 +1,52 @@
+import gradio as gr 
+import json
+import base64
+import requests
+import cv2
+
+def hand_classification(img):
+    # 可选的请求参数
+    # top_num: 返回的分类数量，不声明的话默认为 6 个
+    PARAMS = {"top_num": 2}
+
+    # 服务详情 中的 接口地址
+    MODEL_API_URL = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/classification/handclass"
+
+    # 调用 API 需要 ACCESS_TOKEN。若已有 ACCESS_TOKEN 则于下方填入该字符串
+    # 否则，留空 ACCESS_TOKEN，于下方填入 该模型部署的 API_KEY 以及 SECRET_KEY，会自动申请并显示新 ACCESS_TOKEN
+    ACCESS_TOKEN = ""
+    API_KEY = "TPhSFQU5i8tYivTgsLWBRLi9"
+    SECRET_KEY = "eZbQHnOqTGYBVDXGTqzAy5kvU03t32Qz"
+
+
+    print("1. 读取目标图片 ")
+    success,encoded_image = cv2.imencode(".jpg",img) #注意这句编码，否则图像质量不合格
+    img_test = encoded_image.tobytes()
+    img_test = base64.b64encode(img_test)
+    PARAMS["image"] = img_test.decode('UTF8')
+
+
+    if not ACCESS_TOKEN:
+        print("2. ACCESS_TOKEN 为空，调用鉴权接口获取TOKEN")
+        auth_url = "https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={}&client_secret={}".format(API_KEY, SECRET_KEY)
+        auth_resp = requests.get(auth_url)
+        auth_resp_json = auth_resp.json()
+        ACCESS_TOKEN = auth_resp_json["access_token"]
+        print("新 ACCESS_TOKEN: {}".format(ACCESS_TOKEN))
+    else:
+        print("2. 使用已有 ACCESS_TOKEN")
+
+
+    print("3. 向模型接口 'MODEL_API_URL' 发送请求")
+    request_url = "{}?access_token={}".format(MODEL_API_URL, ACCESS_TOKEN)
+    response = requests.post(url=request_url, json=PARAMS)
+    response_json = response.json()
+    response_str = json.dumps(response_json, indent=4, ensure_ascii=False)
+    print("结果:\n{}".format(response_str))
+    result = response_json["results"]
+    res = {result[0]["name"]:result[0]["score"],result[1]["name"]:result[1]["score"]}
+    return res
+ 
+demo = gr.Interface(fn=hand_classification, inputs="image", outputs="label")
+gr.close_all()
+demo.launch()