Update my_model/tabs/model_arch.py
Browse files
my_model/tabs/model_arch.py
CHANGED
@@ -6,19 +6,18 @@ def run_model_arch() -> None:
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"""
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Displays the model architecture and accompanying abstract and design details for the Knowledge-Based Visual Question
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Answering (KB-VQA) model.
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-
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This function reads an HTML file containing the model architecture and renders it in a Streamlit application.
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It also provides detailed descriptions of the research, abstract, and design of the KB-VQA model.
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-
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Returns:
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None
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"""
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# Read the model architecture HTML file
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with open("
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model_arch_html = f.read()
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col1, col2 = st.columns(
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with col1:
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st.markdown("#### Model Architecture")
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components.html(model_arch_html, height=1400)
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@@ -53,7 +52,8 @@ def run_model_arch() -> None:
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and successfully retrieves the necessary knowledge to answer visual questions.
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</div>
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""", unsafe_allow_html=True)
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-
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st.markdown("#### Design")
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st.markdown("""
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<div style="text-align: justify;">
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@@ -69,4 +69,5 @@ def run_model_arch() -> None:
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(PT-LLMs), which is responsible for performing reasoning and deriving the required knowledge to formulate an
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informed response to the question.
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</div>
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""", unsafe_allow_html=True)
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"""
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Displays the model architecture and accompanying abstract and design details for the Knowledge-Based Visual Question
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Answering (KB-VQA) model.
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This function reads an HTML file containing the model architecture and renders it in a Streamlit application.
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It also provides detailed descriptions of the research, abstract, and design of the KB-VQA model.
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+
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Returns:
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None
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"""
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# Read the model architecture HTML file
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with open("Model Arch.html", 'r', encoding='utf-8') as f:
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model_arch_html = f.read()
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col1, col2 = st.columns(2)
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with col1:
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st.markdown("#### Model Architecture")
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components.html(model_arch_html, height=1400)
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and successfully retrieves the necessary knowledge to answer visual questions.
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</div>
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""", unsafe_allow_html=True)
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+
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st.markdown("<br>" * 4, unsafe_allow_html=True)
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st.markdown("#### Design")
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st.markdown("""
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<div style="text-align: justify;">
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(PT-LLMs), which is responsible for performing reasoning and deriving the required knowledge to formulate an
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informed response to the question.
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</div>
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""", unsafe_allow_html=True)
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+
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