Create app.py

app.py

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+import numpy as np
+from PIL import Image
+import streamlit as st
+from transformers import AutoTokenizer, ViTFeatureExtractor, VisionEncoderDecoderModel 
+
+# Directory path to the saved model on Google Drive
+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
+
+def generate_captions(image):
+    image = Image.open(image).convert("RGB")
+    generated_caption = tokenizer.decode(model.generate(feature_extractor(image, return_tensors="pt").pixel_values.to("cpu"))[0])
+    sentence = generated_caption
+    text_to_remove = "<|endoftext|>"
+    generated_caption = sentence.replace(text_to_remove, "")
+    return generated_caption
+
+# create the Streamlit app
+def app():
+    st.title('Image from your Side, Trending Hashtags from our Side')
+
+    st.write('Upload an image to see what we have in store.')
+
+    # create file uploader
+    uploaded_file = st.file_uploader("Got You Covered, Upload your wish!, magic on the Way! ", type=["jpg", "jpeg", "png"])
+
+    # check if file has been uploaded
+    if uploaded_file is not None:
+        # load the image
+        image = Image.open(uploaded_file).convert("RGB")
+
+        # Image Captions
+        string = generate_captions(uploaded_file)
+
+        st.image(image, caption='The Uploaded File')
+        st.write("First is first captions for your Photo : ", string)
+
+# run the app
+if __name__ == '__main__':
+    app()