Update app.py

app.py

@@ -4,9 +4,8 @@ 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
+import torch
+from diffusers import StableDiffusionPipeline
 
 # authentification
 with open('./bla.yaml') as file:
@@ -23,21 +22,29 @@ 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")
 
+        pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)  
+        pipe = pipe.to("cuda")
+        
+
         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"))
+            generator = torch.Generator("cuda").manual_seed(1024)
+
+            image_gen = pipe(prompt, generator=generator).images[0]
+            st.image(generator)
+
 
-            print(result)
         #     label = teachable_machine_classification(image, 'keras_model_apple.h5')
         #     if label == 0:
         #         st.write("苹果结痂")