app and caption generator file running and working

app.py

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+import streamlit as st
+
+def main():
+
+    st.markdown('<div style="display: flex; justify-content: center;"><p style="font-size: 30px; font-weight: bold;">Artwork Caption Generation</p></div>', unsafe_allow_html=True)
+    col_left, col_right = st.columns(2)
+
+    with col_left:
+        col11, col12 = st.columns(2)
+        with col11:
+            st.image(image="assets\drawing.jpeg")
+        with col12:
+            st.write("<div style='text-align:right;'>Witness the birth of creativity. Our website breathes life into your artistic visions.</div>", unsafe_allow_html=True)
+        col21, col22 = st.columns(2)
+        with col21:
+            st.write("<div style='text-align:left;'>Delve into the depths of inspiration. Explore our platform to give voice to your masterpiece.</div>", unsafe_allow_html=True)
+        with col22:
+            st.image(image="assets\\thinking.jpeg")
+        col31, col32 = st.columns(2)
+        with col31:
+            st.image(image="assets\generate.jpeg")
+        with col32:
+            st.write("<div style='text-align:right;'>Crafting the narrative of your creation. Unleash your imagination with our caption generator for artwork.</div>", unsafe_allow_html=True)
+
+    with col_right:
+        st.subheader('How to use')
+
+        if st.button(label="Generator", type="primary"):
+            st.switch_page(page='pages/Caption_Generator.py')
+
+if __name__ == "__main__":
+    main()

assets/to be added text.txt

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+
+
+
+Witness the birth of creativity. Our website breathes life into your artistic visions.
+
+
+Delve into the depths of inspiration. Explore our platform to give voice to your masterpiece.
+
+
+Crafting the narrative of your creation. Unleash your imagination with our caption generator for artwork.

pages/Caption_Generator.py

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+import torch
+import numpy as np
+from PIL import Image
+import streamlit as st
+from torchvision.transforms import v2
+from transformers import GenerationConfig
+from transformers import GPT2TokenizerFast
+from transformers import ViTImageProcessor
+from transformers import VisionEncoderDecoderModel
+
+# Page configuration settings
+st.set_page_config(
+    layout="centered",
+    page_title="Generate Caption",
+    initial_sidebar_state="auto",
+)
+
+# Initializing session state keys
+if all(key not in st.session_state.keys() for key in ("generate", "image")):
+    st.session_state["generate"] = False
+    st.session_state["image"] = None
+
+# Loading necessary resources and caching them
+@st.cache_resource(show_spinner="Loading Resources...")
+def loadResources():
+    encoder = 'microsoft/swin-base-patch4-window7-224-in22k'
+    decoder = 'gpt2'
+
+    model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
+        encoder, decoder
+    )
+
+    processor = ViTImageProcessor.from_pretrained(encoder)
+    tokenizer = GPT2TokenizerFast.from_pretrained(decoder)
+
+    if 'gpt2' in decoder:
+        tokenizer.pad_token = tokenizer.eos_token
+        model.config.eos_token_id = tokenizer.eos_token_id
+        model.config.pad_token_id = tokenizer.pad_token_id
+        model.config.decoder_start_token_id = tokenizer.bos_token_id
+    else:
+        model.config.decoder_start_token_id = tokenizer.cls_token_id
+        model.config.pad_token_id = tokenizer.pad_token_id
+
+    model = torch.load("generator_model.pkl", map_location=torch.device("cpu"))
+    model.eval()
+    return processor, tokenizer, model
+
+# Pre-processing image and caching
+@st.cache_data
+def preprocess_image(_processor, image):
+    transforms = v2.Compose([
+        v2.Resize(size=(224,224)),
+        v2.ToDtype(torch.float32, scale = True),
+    ])
+    image = transforms(image)
+    img = _processor(image, return_tensors = 'pt')
+    return img
+
+# Generating caption and caching 
+@st.cache_data
+def get_caption(_processor, _tokenizer, _model, image):
+    image = preprocess_image(_processor, image)
+    output = _model.generate(
+        **image, 
+        generation_config = GenerationConfig(
+            pad_token_id = _tokenizer.pad_token_id
+        )
+    )
+
+    caption = _tokenizer.batch_decode(
+        output,
+        skip_special_tokens = True
+    )
+
+    return caption[0]
+
+# Displaying elements
+def DisplayInteractionElements():
+    st.markdown('<div style="display: flex; justify-content: center;"><p style="font-size: 40px; font-weight: bold;">👉 Caption Generator 👈</p></div>', unsafe_allow_html=True)
+    st.file_uploader(accept_multiple_files=False, label='Upload an Image', type=['jpg', 'jpeg', 'png'], key="image_uploader")
+
+    if st.session_state['image_uploader']:
+        image = st.session_state['image_uploader']
+        im_file = Image.open(image).convert("RGB")
+        im_file = np.array(im_file)
+        
+        st.session_state['image'] = im_file
+        
+        col1, col2, col3 = st.columns(3)
+        
+        col2.image(image=image, caption='Uploaded Image')
+        
+        st.button(label='Generate Caption', use_container_width=True, type='primary', on_click=generateCaption)
+
+# Triggering generate state
+def generateCaption():
+        st.session_state['generate'] = True
+
+def main():
+
+    DisplayInteractionElements()
+
+    processor, tokenizer, model = loadResources()
+
+    if not st.session_state['image_uploader']:
+        st.session_state['generate'] = False
+
+    if st.session_state['generate'] and st.session_state['image_uploader']:
+        caption = get_caption(processor, tokenizer, model, st.session_state['image'])
+        st.markdown(f'<div style="display: flex; justify-content: center;"><p style="font-size: 35px; font-weight: bold; color: blue;">{caption}</p></div>', unsafe_allow_html = True)
+
+
+if __name__ == "__main__":
+    main()
+
+
+
+
+