app.py

from io import BytesIO
import streamlit as st
import pandas as pd
import json
import os
import numpy as np
from streamlit.elements import markdown
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()


@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(persist=True)
def generate_sequence(pixel_values, lang_code, num_beams, temperature, top_p):
    lang_code = language_mapping[lang_code]
    output_ids = model.generate(input_ids=pixel_values, forced_bos_token_id=tokenizer.lang_code_to_id[lang_code], max_length=64, num_beams=num_beams, temperature=temperature, top_p = top_p)
    print(output_ids)
    output_sequence = tokenizer.batch_decode(output_ids[0], skip_special_tokens=True, max_length=64)
    return output_sequence

def read_markdown(path, parent="./sections/"):
    with open(os.path.join(parent, path)) as f:
        return f.read()


checkpoints = ["./ckpt/ckpt-17499"]  # 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")
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 = 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 = 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}")


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", caption="Seq2Seq model for Image-text Captioning."
    # )
    st.markdown(read_markdown("pretraining.md"))
    st.write(read_markdown("challenges.md"))
    st.write(read_markdown("social_impact.md"))
    st.write(read_markdown("references.md"))
    # st.write(read_markdown("checkpoints.md"))
    st.write(read_markdown("acknowledgements.md"))


first_index = 20
# 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

# col1, col2 = st.beta_columns([6, 4])

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

# col2.write("OR")

# uploaded_file = col2.file_uploader("Upload your image", type=["png", "jpg", "jpeg"])
# if uploaded_file is not None:
#     state.image_file = os.path.join("images", uploaded_file.name)
#     state.image = np.array(Image.open(uploaded_file))

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
new_col2.write("**Reference Caption**: " + state.caption)
new_col2.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."
)

with st.spinner("Loading model..."):
    model = load_model(checkpoints[0])
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)
# print(sequence)

if sequence!=['']:
    st.write(
        "**Generated Caption**: "+sequence[0]
    )

    st.write(
        "**English Translation**: "+ sequence[0] if lang_id=="en" else translate(sequence[0])
    )