Update app.py

app.py

@@ -2,52 +2,54 @@ import streamlit as st
 import torch
 from PIL import Image
 from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
-st.title("Image_Captioning_App")
-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")
+@st.cache
+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
 #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"
-
 c2, c3 = st.columns([2,1])
 def change_photo_state():
 	st.session_state["photo"]="done"
-print("="*150)
-print("RESNET MODEL LOADED")
-
 @st.cache
 def load_image(img):
 	im = Image.open(img)
 	return im
 uploaded_photo = c2.file_uploader("Upload Image",type=['jpg','png','jpeg'], on_change=change_photo_state)
 camera_photo = c2.camera_input("Take a photo", on_change=change_photo_state)
-st.subheader("The following activity is occurring ...") 
-if st.session_state["photo"]=="done":
-   if uploaded_photo:
-      our_image= load_image(uploaded_photo)
-   elif camera_photo:
-      our_image= load_image(camera_photo)
-   elif uploaded_photo==None and camera_photo==None:
-      our_image= load_image('image.jpg')
+#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
    num_beams = 4
    gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
    def predict_step(our_image):
-       if our_image.mode != "RGB":
-          our_image = our_image.convert(mode="RGB")
-       pixel_values = feature_extractor(images=our_image, return_tensors="pt").pixel_values
-       pixel_values = pixel_values.to(device)
-       output_ids = model.generate(pixel_values, **gen_kwargs)
-       preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-       preds = [pred.strip() for pred in preds]
-       return preds
-   st.success(predict_step(our_image))
-if st.checkbox('About'):
+      if our_image.mode != "RGB":
+         our_image = our_image.convert(mode="RGB")
+      pixel_values = feature_extractor(images=our_image, return_tensors="pt").pixel_values
+      pixel_values = pixel_values.to(device)
+      output_ids = model.generate(pixel_values, **gen_kwargs)
+      preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+      preds = [pred.strip() for pred in preds]
+      return preds
+   if st.session_state["photo"]=="done":
+      if uploaded_photo:
+         our_image= load_image(uploaded_photo)
+      elif camera_photo:
+         our_image= load_image(camera_photo)
+      elif uploaded_photo==None and camera_photo==None:
+         our_image= load_image('image.jpg')
+      st.success(predict_step(our_image))
+elif st.checkbox("About"):
    st.subheader("About Image Captioning App")
-   st.markdown("Built with Streamlit by [Soumen Sarker](https://soumen-sarker-personal-site.streamlit.app/)")
+   st.markdown("Built with Streamlit by [Soumen Sarker](https://soumen-sarker-personal-website.streamlit.app/)")
    st.markdown("Demo applicaton of the following model [credit](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning/)")