diff --git a/app.py b/app.py index 5e8180ea0b970742d86d32b39df024365c3dd7c7..aaed2053513768bbfca1b545afa774c15840e6c6 100644 --- a/app.py +++ b/app.py @@ -1,4 +1,5 @@ import streamlit as st +import requests # Designing the interface @@ -7,56 +8,60 @@ st.write("[Yih-Dar SHIEH](https://huggingface.co/ydshieh)") st.sidebar.markdown( """ - An image captioning model [ViT-GPT2](https://huggingface.co/flax-community/vit-gpt2) by combining the ViT model with the GPT2 model. - [Part of the [Huggingface JAX/Flax event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/).]\n - The encoder (ViT) and decoder (GPT2) are combined using Hugging Face transformers' `FlaxVisionEncoderDecoderModel`. + An image captioning model by combining ViT model with GPT2 model. + The encoder (ViT) and decoder (GPT2) are combined using Hugging Face transformers' [Vision-To-Text Encoder-Decoder + framework](https://huggingface.co/transformers/master/model_doc/visionencoderdecoder.html). The pretrained weights of both models are loaded, with a set of randomly initialized cross-attention weights. The model is trained on the COCO 2017 dataset for about 6900 steps (batch_size=256). + [Follow-up work of [Huggingface JAX/Flax event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/).]\n """ ) -#image = Image.open('samples/val_000000039769.jpg') -#show = st.image(image, use_column_width=True) -#show.image(image, 'Preloaded Image', use_column_width=True) - - with st.spinner('Loading and compiling ViT-GPT2 model ...'): - from model import * - # st.sidebar.write(f'Vit-GPT2 model loaded :)') -st.sidebar.title("Select a sample image") -sample_name = st.sidebar.selectbox( - "Please choose an image", - sample_fns +st.sidebar.title("Select a sample image") +image_id = st.sidebar.selectbox( + "Please choose a sample image", + sample_image_ids ) -sample_name = f"COCO_val2014_{sample_name.replace('.jpg', '').zfill(12)}.jpg" +random_image_id = None +if st.sidebar.button("Random COCO 2017 (val) images"): + random_image_id = get_random_image_id() + +if random_image_id is not None: + image_id = random_image_id + +st.write(image_id) + +sample_name = f"COCO_val2017_{str(image_id).zfill(12)}.jpg" sample_path = os.path.join(sample_dir, sample_name) -image = Image.open(sample_path) -show = st.image(image, width=480) -show.image(image, '\n\nSelected Image', width=480) +if os.path.isfile(sample_path): + image = Image.open(sample_path) +else: + url = f"http://images.cocodataset.org/val2017/{str(image_id).zfill(12)}.jpg" + image = Image.open(requests.get(url, stream=True).raw) + +resized = image.resize(size=(384, 384)) +show = st.image(resized, width=384) +show.image(resized, '\n\nSelected Image', width=384) +resized.close() # For newline st.sidebar.write('\n') - with st.spinner('Generating image caption ...'): caption = predict(image) caption_en = caption - st.header(f'**Prediction (in English)**: {caption_en}') - - # caption_en = translator.translate(caption, src='fr', dest='en').text - # st.header(f'**Prediction (in French) **{caption}') - # st.header(f'**English Translation**: {caption_en}') - + st.header(f'Predicted caption:\n\n') + st.subheader(caption_en) st.sidebar.header("ViT-GPT2 predicts:") st.sidebar.write(f"**English**: {caption}") - image.close() diff --git a/model.py b/model.py index efb180d927250d37b4441b76652367c5c5f3ce83..9d7dc9581be12e696e7ad58e773767b69e4444ba 100644 --- a/model.py +++ b/model.py @@ -1,12 +1,13 @@ +import json import os, shutil +import random + + from PIL import Image import jax from transformers import FlaxVisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer from huggingface_hub import hf_hub_download -from googletrans import Translator -translator = Translator() - # create target model directory model_dir = './models/' @@ -65,4 +66,13 @@ _compile() sample_dir = './samples/' -sample_fns = tuple([f"{int(f.replace('COCO_val2014_', '').replace('.jpg', ''))}.jpg" for f in os.listdir(sample_dir) if f.startswith('COCO_val2014_')]) +sample_image_ids = tuple([int(f.replace('COCO_val2017_', '').replace('.jpg', '')) for f in os.listdir(sample_dir) if f.startswith('COCO_val2017_')]) + +with open(os.path.join(sample_dir, "coco-val2017-img-ids.json"), "r", encoding="UTF-8") as fp: + coco_2017_val_image_ids = json.load(fp) + + +def get_random_image_id(): + + image_id = random.sample(coco_2017_val_image_ids, k=1)[0] + return image_id diff --git a/samples/COCO_val2014_000000581632.jpg b/samples/COCO_val2014_000000581632.jpg deleted file mode 100644 index d3de1bd8a9db8c3b21107f8fd8516a3260bbd5a2..0000000000000000000000000000000000000000 Binary files a/samples/COCO_val2014_000000581632.jpg and /dev/null differ diff --git a/samples/COCO_val2014_000000581683.jpg b/samples/COCO_val2014_000000581683.jpg deleted file mode 100644 index 1763ecf7ec518cb032086eecc3e9744c1936605d..0000000000000000000000000000000000000000 Binary files a/samples/COCO_val2014_000000581683.jpg and /dev/null differ diff --git a/samples/COCO_val2014_000000581702.jpg 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