Create app.py

app.py

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+import streamlit as st
+# from img_classification import teachable_machine_classification
+from PIL import Image, ImageOps
+import streamlit_authenticator as stauth
+import yaml
+from yaml.loader import SafeLoader
+import numpy as np
+from deepface import DeepFace
+import cv2
+
+# authentification
+with open('./bla.yaml') as file:
+    config = yaml.load(file, Loader=SafeLoader)
+authenticator = stauth.Authenticate(
+    config['credentials'],
+    config['cookie']['name'],
+    config['cookie']['key'],
+    config['cookie']['expiry_days'],
+    config['preauthorized']
+)
+
+name, authentication_status, username = authenticator.login('Login', 'main')
+if authentication_status:
+    authenticator.logout('Logout', 'main')
+    page = st.sidebar.selectbox("探索或预测", ("苹果病分类","bla"))
+
+    if page == "苹果病分类":
+        st.title("使用谷歌的可教机器进行图像分类")
+        st.header("苹果病")
+        st.text("上传彩色苹果叶子图片")
+
+        uploaded_file = st.file_uploader("选择..", type=["jpg","png","jpeg"])
+        if uploaded_file is not None:
+            image = Image.open(uploaded_file).convert('RGB')
+            st.image(image, caption='上传了图片。', use_column_width=True)
+            st.write("")
+            st.write("分类...")
+            result = DeepFace.analyze(image,actions=("gender","age"))
+
+            print(result)
+        #     label = teachable_machine_classification(image, 'keras_model_apple.h5')
+        #     if label == 0:
+        #         st.write("苹果结痂")
+        #     elif label == 1:
+        #         st.write("黑腐病")
+        #     elif label == 2:
+        #         st.write("雪松苹果锈")
+        #     else:
+        #         st.write("健康苹果")
+
+        # st.text("类:苹果结痂, 黑腐病, 雪松苹果锈, 健康苹果")
+
+        # 0 apple_scrab
+        # 1 black_rot
+        # 2 cedar_apple_rust
+        # 3 apple_healthy
+
+    
+elif authentication_status == False:
+    st.error('Username/password is incorrect')
+elif authentication_status == None:
+    st.warning('Please enter your username and password')
+