from apps import article, mic import streamlit as st from session import _get_state from multiapp import MultiApp # from io import BytesIO # from apps.utils import read_markdown # from apps import article # import streamlit as st # import pandas as pd # 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, # ) # import matplotlib.pyplot as plt # from mtranslate import translate # from session import _get_state # state = _get_state() # @st.cache # 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", # } # @st.cache # 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 # checkpoints = ["./ckpt/ckpt-51999"] # TODO: Maybe add more checkpoints? # dummy_data = pd.read_csv("reference.tsv", sep="\t") # 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("## 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: 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 , 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. es un estudiante de posgrado.', use_column_width='always') # with col3: # st.image("./misc/examples/women_cricket.jpg", width=350, caption = 'English Caption: of India bats against 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: auf dem Motorrad von .', 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]) # ) def main(): state = _get_state() 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("Multilingual Image Captioning") logo = st.sidebar.image("./misc/mic-logo.png") st.sidebar.write("Multilingual Image Captioning addresses the challenge of caption generation for an image in a multilingual setting. Here, we fuse CLIP Vision transformer into mBART50 and perform training on translated version of Conceptual-12M dataset. Please use the radio buttons below to navigate.") app = MultiApp(state) app.add_app("Article", article.app) app.add_app("Multilingual Image Captioning", mic.app) # app.add_app("Mask Filling", mlm.app) app.run() state.sync() if __name__ == "__main__": main()