upload scripts

app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+
+
+# Designing the interface
+st.title("French Image Caption App")
+# For newline
+st.write('\n')
+
+image = Image.open('images/image.png')
+show = st.image(image, use_column_width=True)
+# show.image(image, 'Preloaded Image', use_column_width=True)
+
+with st.spinner('Loading ViT-GPT2 model ...'):
+
+    from model import *
+    st.sidebar.write(f'Vit-GPT2 model loaded :)')
+
+st.sidebar.title("Upload Image")
+
+# Disabling warning
+st.set_option('deprecation.showfileUploaderEncoding', False)
+# Choose your own image
+uploaded_file = st.sidebar.file_uploader(" ", type=['png', 'jpg', 'jpeg'])
+
+if uploaded_file is not None:
+
+    image = Image.open(uploaded_file)
+    show.image(image, 'Uploaded Image', use_column_width=True)
+
+
+# For newline
+st.sidebar.write('\n')
+
+if st.sidebar.button("Click here to get image caption"):
+
+    if uploaded_file is None:
+
+        st.sidebar.write("Please upload an Image to Classify")
+
+    else:
+
+        with st.spinner('Generating image caption ...'):
+
+            caption, tokens, token_ids = predict(image)
+
+            st.success(f'caption: {caption}')
+            st.success(f'tokens: {tokens}')
+            st.success(f'token ids: {token_ids}')
+
+        st.sidebar.header("ViT-GPT2 predicts:")
+        st.sidebar.write(f"caption: {caption}", '\n')

model.py

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+def predict(image):
+
+    return 'dummy caption!', ['dummy', 'caption', '!'], [1, 2, 3]
+
+
+
+
+
+
+