Update app.py

app.py

@@ -3,16 +3,15 @@ import torch
 from PIL import Image
 from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
 @st.cache
+#pickle.load(open('energy_model.pkl', 'rb'))
+#vocab = np.load('w2i.p', allow_pickle=True)
+st.title("Image_Captioning_App")
+@st.experimental_singleton
 def load_models():
     model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
     feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
     tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
     return model, feature_extractor, tokenizer
-model, feature_extractor, tokenizer = load_models()
-#pickle.load(open('energy_model.pkl', 'rb'))
-#vocab = np.load('w2i.p', allow_pickle=True)
-st.title("Image_Captioning_App")
-
 #st.text("Build with Streamlit and OpenCV")
 if "photo" not in st.session_state:
 	st.session_state["photo"]="not done"
@@ -28,6 +27,7 @@ camera_photo = c2.camera_input("Take a photo", on_change=change_photo_state)
 
 #st.subheader("Detection")
 if st.checkbox("Generate_Caption"):
+   model, feature_extractor, tokenizer = load_models()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    max_length = 16