pages/15_MiniGemini.py

import streamlit as st
from streamlit_extras.switch_page_button import switch_page


translations = {
'en': {'title': 'MiniGemini',
    'original_tweet': 
       """
       [Original tweet](https://x.com/mervenoyann/status/1783864388249694520) (April 26, 2024)
       """,
    'tweet_1':
        """
        MiniGemini is the coolest VLM, let's explain 🧶
        """,
    'tweet_2':
        """
        MiniGemini is a vision language model that understands both image and text and also generates text and an image that goes best with the context! 🤯
        """,
    'tweet_3':
        """
        This model has two image encoders (one CNN and one ViT) in parallel to capture the details in the images.
        I saw the same design in <a href='DocOwl_1.5' target='_self'>DocOwl 1.5</a> then it has a decoder to output text and also a prompt to be sent to SDXL for image generation (which works very well!)
        """,
    'tweet_4':
        """
        They adopt CLIP's ViT for low resolution visual embedding encoder and a CNN-based one for high resolution image encoding (precisely a pre-trained ConvNeXt).
        """,
    'tweet_5':
        """
        Thanks to the second encoder it can grasp details in images, which also comes in handy for e.g. document tasks (but see below the examples are mindblowing IMO).
        """,
    'tweet_6':
        """
        According to their reporting the model performs very well across many benchmarks compared to LLaVA 1.5 and Gemini Pro.
        """,
    'ressources':
        """
        Resources:  
        [Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models](https://huggingface.co/papers/2403.18814) 
        by Yanwei Li, Yuechen Zhang, Chengyao Wang, Zhisheng Zhong, Yixin Chen, Ruihang Chu, Shaoteng Liu, Jiaya Jia (2024)   
        [GitHub](https://github.com/dvlab-research/MGM)  
        [Model Repository](https://huggingface.co/YanweiLi/MGM-13B-HD)
        """
      },
'fr': {
    'title': 'MiniGemini',
    'original_tweet': 
       """
       [Tweet de base](https://x.com/mervenoyann/status/1783864388249694520) (26 avril 2024)
       """,
    'tweet_1':
        """
        MiniGemini est le VLM le plus cool, voici pourquoi 🧶
        """,
    'tweet_2':
        """
        MiniGemini est un modèle de langage/vision qui comprend à la fois l'image et le texte et qui génère également le texte et l'image qui s'accordent le mieux avec le contexte ! 🤯        """,
    'tweet_3':
        """
        Ce modèle possède deux encodeurs d'images (un ConvNet et un ViT) en parallèle pour capturer les détails dans les images.
        J'ai vu la même conception dans <a href='DocOwl 1.5' target='_self'>DocOwl 1.5</a> où il y a un décodeur pour produire du texte et aussi un prompt à envoyer au SDXL pour la génération d'images (qui fonctionne très bien !).        """,
    'tweet_4':
        """
        Les auteurs adoptent le ViT de CLIP pour les enchâssements visuels de basse résolution et un ConvNet pour les images en haute résolution (précisément un ConvNeXt pré-entraîné).
        """,
    'tweet_5':
        """
        Grâce au second encodeur, il peut saisir des détails dans les images, ce qui s'avère également utile pour les tâches documentaires (voir ci-dessous les exemples époustouflants).        """,
    'tweet_6':
        """
        D'après leur rapport, le modèle est très performant dans de nombreux benchmarks par rapport à LLaVA 1.5 et Gemini Pro.
        """,
    'ressources':
        """
        Resources :  
        [Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models](https://huggingface.co/papers/2403.18814) 
        de Yanwei Li, Yuechen Zhang, Chengyao Wang, Zhisheng Zhong, Yixin Chen, Ruihang Chu, Shaoteng Liu, Jiaya Jia (2024)   
        [GitHub](https://github.com/dvlab-research/MGM)  
        [Modèle](https://huggingface.co/YanweiLi/MGM-13B-HD)
        """
    }
}    


def language_selector():
    languages = {'EN': '🇬🇧', 'FR': '🇫🇷'}
    selected_lang = st.selectbox('', options=list(languages.keys()), format_func=lambda x: languages[x], key='lang_selector')
    return 'en' if selected_lang == 'EN' else 'fr'

left_column, right_column = st.columns([5, 1])

# Add a selector to the right column
with right_column:
    lang = language_selector()

# Add a title to the left column
with left_column:
    st.title(translations[lang]["title"])
    
st.success(translations[lang]["original_tweet"], icon="ℹ️")
st.markdown(""" """)

st.markdown(translations[lang]["tweet_1"], unsafe_allow_html=True)
st.markdown(""" """)

st.image("pages/MiniGemini/image_1.jpg", use_container_width=True)
st.markdown(""" """)

st.markdown(translations[lang]["tweet_2"], unsafe_allow_html=True)
st.markdown(""" """)

st.image("pages/MiniGemini/image_2.jpg", use_container_width=True)
st.markdown(""" """)

st.markdown(translations[lang]["tweet_3"], unsafe_allow_html=True)
st.markdown(""" """)

st.image("pages/MiniGemini/image_3.jpg", use_container_width=True)
st.markdown(""" """)

st.markdown(translations[lang]["tweet_4"], unsafe_allow_html=True)
st.markdown(""" """)

st.image("pages/MiniGemini/image_4.jpg", use_container_width=True)
st.markdown(""" """)

st.markdown(translations[lang]["tweet_5"], unsafe_allow_html=True)
st.markdown(""" """)

st.image("pages/MiniGemini/image_5.jpg", use_container_width=True)
st.markdown(""" """)

st.markdown(translations[lang]["tweet_6"], unsafe_allow_html=True)
st.markdown(""" """)

st.image("pages/MiniGemini/image_6.jpg", use_container_width=True)
st.markdown(""" """)

st.info(translations[lang]["ressources"], icon="📚")  

st.markdown(""" """)
st.markdown(""" """)
st.markdown(""" """)
col1, col2, col3= st.columns(3)
with col1:
    if lang == "en":
        if st.button('Previous paper', use_container_width=True):
            switch_page("DocOwl 1.5")
    else:
        if st.button('Papier précédent', use_container_width=True):
            switch_page("DocOwl 1.5")
with col2:
    if lang == "en":
        if st.button("Home", use_container_width=True):
            switch_page("Home")
    else:
        if st.button("Accueil", use_container_width=True):
            switch_page("Home")
with col3:
    if lang == "en":
        if st.button("Next paper", use_container_width=True):
            switch_page("CuMo")
    else:
        if st.button("Papier suivant", use_container_width=True):
            switch_page("PLLaVA")