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| from io import BytesIO | |
| import streamlit as st | |
| import pandas as pd | |
| import json | |
| import os | |
| import numpy as np | |
| from streamlit import caching | |
| from PIL import Image | |
| from model.flax_clip_vision_mbart.modeling_clip_vision_mbart import ( | |
| FlaxCLIPVisionMBartForConditionalGeneration, | |
| ) | |
| from transformers import MBart50TokenizerFast | |
| from utils import ( | |
| get_transformed_image, | |
| ) | |
| import matplotlib.pyplot as plt | |
| from mtranslate import translate | |
| from session import _get_state | |
| state = _get_state() | |
| def load_model(ckpt): | |
| return FlaxCLIPVisionMBartForConditionalGeneration.from_pretrained(ckpt) | |
| tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50") | |
| language_mapping = { | |
| "en": "en_XX", | |
| "de": "de_DE", | |
| "fr": "fr_XX", | |
| "es": "es_XX" | |
| } | |
| code_to_name = { | |
| "en": "English", | |
| "fr": "French", | |
| "de": "German", | |
| "es": "Spanish", | |
| } | |
| def generate_sequence(pixel_values, lang_code, num_beams, temperature, top_p, do_sample, top_k, max_length): | |
| lang_code = language_mapping[lang_code] | |
| output_ids = state.model.generate(input_ids=pixel_values, forced_bos_token_id=tokenizer.lang_code_to_id[lang_code], max_length=max_length, num_beams=num_beams, temperature=temperature, top_p = top_p, top_k=top_k, do_sample=do_sample) | |
| print(output_ids) | |
| output_sequence = tokenizer.batch_decode(output_ids[0], skip_special_tokens=True, max_length=max_length) | |
| return output_sequence | |
| def read_markdown(path, parent="./sections/"): | |
| with open(os.path.join(parent, path)) as f: | |
| return f.read() | |
| checkpoints = ["./ckpt/ckpt-49499"] # TODO: Maybe add more checkpoints? | |
| dummy_data = pd.read_csv("reference.tsv", sep="\t") | |
| st.set_page_config( | |
| page_title="Multilingual Image Captioning", | |
| layout="wide", | |
| initial_sidebar_state="collapsed", | |
| page_icon="./misc/mic-logo.png", | |
| ) | |
| st.title("Multilingual Image Captioning") | |
| st.write( | |
| "[Bhavitvya Malik](https://huggingface.co/bhavitvyamalik), [Gunjan Chhablani](https://huggingface.co/gchhablani)" | |
| ) | |
| st.sidebar.title("Generation Parameters") | |
| # max_length = st.sidebar.number_input("Max Length", min_value=16, max_value=128, value=64, step=1, help="The maximum length of sequence to be generated.") | |
| max_length = 64 | |
| do_sample = st.sidebar.checkbox("Sample", value=False, help="Sample from the model instead of using beam search.") | |
| top_k = st.sidebar.number_input("Top K", min_value=10, max_value=200, value=50, step=1, help="The number of highest probability vocabulary tokens to keep for top-k-filtering.") | |
| num_beams = st.sidebar.number_input(label="Number of Beams", min_value=2, max_value=10, value=4, step=1, help="Number of beams to be used in beam search.") | |
| temperature = st.sidebar.select_slider(label="Temperature", options = list(np.arange(0.0,1.1, step=0.1)), value=1.0, help ="The value used to module the next token probabilities.", format_func=lambda x: f"{x:.2f}") | |
| top_p = st.sidebar.select_slider(label = "Top-P", options = list(np.arange(0.0,1.1, step=0.1)),value=1.0, help="Nucleus Sampling : If set to float < 1, only the most probable tokens with probabilities that add up to :obj:`top_p` or higher are kept for generation.", format_func=lambda x: f"{x:.2f}") | |
| if st.sidebar.button("Clear All Cache"): | |
| caching.clear_cache() | |
| image_col, intro_col = st.beta_columns([3, 8]) | |
| image_col.image("./misc/mic-logo.png", use_column_width="always") | |
| intro_col.write(read_markdown("intro.md")) | |
| with st.beta_expander("Usage"): | |
| st.markdown(read_markdown("usage.md")) | |
| with st.beta_expander("Article"): | |
| st.write(read_markdown("abstract.md")) | |
| st.write(read_markdown("caveats.md")) | |
| st.write("## Methodology") | |
| st.image( | |
| "./misc/Multilingual-IC.png" | |
| ) | |
| st.markdown(read_markdown("pretraining.md")) | |
| st.write(read_markdown("challenges.md")) | |
| st.write(read_markdown("social_impact.md")) | |
| st.write(read_markdown("bias.md")) | |
| col1, col2, col3, col4 = st.beta_columns([0.5,2.5,2.5,0.5]) | |
| with col2: | |
| st.image("./misc/examples/female_dev_1.jpg", width=350, caption = 'German Caption: <PERSON> arbeitet an einem Computer.', use_column_width='always') | |
| with col3: | |
| st.image("./misc/examples/female_doctor.jpg", width=350, caption = 'English Caption: A portrait of <PERSON>, a doctor who specializes in health care.', use_column_width='always') | |
| col1, col2, col3, col4 = st.beta_columns([0.5,2.5,2.5,0.5]) | |
| with col2: | |
| st.image("./misc/examples/female_doctor_1.jpg", width=350, caption = 'Spanish Caption: El Dr. <PERSON> es un estudiante de posgrado.', use_column_width='always') | |
| with col3: | |
| st.image("./misc/examples/women_cricket.jpg", width=350, caption = 'English Caption: <PERSON> of India bats against <PERSON> of Australia during the first Twenty20 match between India and Australia at Indian Bowl Stadium in New Delhi on Friday. - PTI', use_column_width='always') | |
| col1, col2, col3, col4 = st.beta_columns([0.5,2.5,2.5,0.5]) | |
| with col2: | |
| st.image("./misc/examples/female_dev_2.jpg", width=350, caption = "French Caption: Un écran d'ordinateur avec un écran d'ordinateur ouvert.", use_column_width='always') | |
| with col3: | |
| st.image("./misc/examples/female_biker_resized.jpg", width=350, caption = 'German Caption: <PERSON> auf dem Motorrad von <PERSON>.', use_column_width='always') | |
| st.write(read_markdown("future_scope.md")) | |
| st.write(read_markdown("references.md")) | |
| # st.write(read_markdown("checkpoints.md")) | |
| st.write(read_markdown("acknowledgements.md")) | |
| if state.model is None: | |
| with st.spinner("Loading model..."): | |
| state.model = load_model(checkpoints[0]) | |
| first_index = 25 | |
| # Init Session State | |
| if state.image_file is None: | |
| state.image_file = dummy_data.loc[first_index, "image_file"] | |
| state.caption = dummy_data.loc[first_index, "caption"].strip("- ") | |
| state.lang_id = dummy_data.loc[first_index, "lang_id"] | |
| image_path = os.path.join("images", state.image_file) | |
| image = plt.imread(image_path) | |
| state.image = image | |
| if st.button("Get a random example", help="Get a random example from one of the seeded examples."): | |
| sample = dummy_data.sample(1).reset_index() | |
| state.image_file = sample.loc[0, "image_file"] | |
| state.caption = sample.loc[0, "caption"].strip("- ") | |
| state.lang_id = sample.loc[0, "lang_id"] | |
| image_path = os.path.join("images", state.image_file) | |
| image = plt.imread(image_path) | |
| state.image = image | |
| transformed_image = get_transformed_image(state.image) | |
| new_col1, new_col2 = st.beta_columns([5,5]) | |
| # Display Image | |
| new_col1.image(state.image, use_column_width="always") | |
| # Display Reference Caption | |
| with new_col1.beta_expander("Reference Caption"): | |
| st.write("**Reference Caption**: " + state.caption) | |
| st.markdown( | |
| f"""**English Translation**: {state.caption if state.lang_id == "en" else translate(state.caption, 'en')}""" | |
| ) | |
| # Select Language | |
| options = list(code_to_name.keys()) | |
| lang_id = new_col2.selectbox( | |
| "Language", | |
| index=options.index(state.lang_id), | |
| options=options, | |
| format_func=lambda x: code_to_name[x], | |
| help="The language in which caption is to be generated." | |
| ) | |
| sequence = [''] | |
| if new_col2.button("Generate Caption", help="Generate a caption in the specified language."): | |
| with st.spinner("Generating Sequence..."): | |
| sequence = generate_sequence(transformed_image, lang_id, num_beams, temperature, top_p, do_sample, top_k, max_length) | |
| # print(sequence) | |
| if sequence!=['']: | |
| new_col2.write( | |
| "**Generated Caption**: "+sequence[0] | |
| ) | |
| new_col2.write( | |
| "**English Translation**: "+ sequence[0] if lang_id=="en" else translate(sequence[0]) | |
| ) | |