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Update app.py
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app.py
CHANGED
@@ -4,19 +4,25 @@ from PIL import Image, ImageDraw, ImageFont
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import io
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def main():
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st.markdown(
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"""
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<style>
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.stMultiSelect [data-baseweb="tag"] {
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background-color: #3fa45bff !important;
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color: white !important;
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font-weight: medium;
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border-radius: 5px;
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padding: 5px 10px;
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}
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.stMultiSelect [data-baseweb="tag"]:hover {
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background-color: #358d4d !important;
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}
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.stMultiSelect input {
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color: black !important;
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}
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@@ -25,33 +31,55 @@ def main():
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unsafe_allow_html=True,
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)
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with st.sidebar:
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col1, col2 = st.columns([1, 5])
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with col1:
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logo = Image.open("logo.png")
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resized_logo = logo.resize((50, 50))
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st.image(resized_logo)
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with col2:
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st.markdown(
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"""
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<div style="
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AI Energy Score
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</div>
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""",
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unsafe_allow_html=True,
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)
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-
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st.sidebar.markdown("<hr style='border: 1px solid gray; margin: 15px 0;'>", unsafe_allow_html=True)
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st.sidebar.write("### Generate Label:")
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-
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task_order = [
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"Text Generation",
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"
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]
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-
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st.sidebar.write("#### 1. Select task(s) to view models")
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selected_tasks = st.sidebar.multiselect("", options=task_order, default=["Text Generation"])
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task_to_file = {
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"Text Generation": "text_gen_energyscore.csv",
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"Image Generation": "image_generation_energyscore.csv",
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@@ -64,7 +92,6 @@ def main():
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"Question Answering": "question_answering_energyscore.csv",
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"Sentence Similarity": "sentence_similarity_energyscore.csv"
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}
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st.sidebar.write("#### 2. Select a model to generate label")
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default_model_data = {
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'provider': "AI Provider",
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@@ -76,7 +103,6 @@ def main():
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'energy': "?",
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'score': 5
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}
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if not selected_tasks:
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model_data = default_model_data
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else:
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@@ -94,7 +120,8 @@ def main():
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df['full_model'] = df['model']
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df[['provider', 'model']] = df['model'].str.split(pat='/', n=1, expand=True)
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df['score'] = df['energy_score'].fillna(1).astype(int)
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df['date'] = "February 2025"
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df['hardware'] = "NVIDIA H100-80GB"
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@@ -118,6 +145,7 @@ def main():
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model_data = data_df[data_df["full_model"] == selected_model].iloc[0]
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st.sidebar.write("#### 3. Download the label")
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try:
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score = int(model_data["score"])
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background_path = f"{score}.png"
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@@ -130,7 +158,8 @@ def main():
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return
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final_size = (520, 728)
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generated_label = background
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st.image(generated_label, caption="Generated Label Preview", width=520)
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img_buffer = io.BytesIO()
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@@ -144,5 +173,66 @@ def main():
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mime="image/png"
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)
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if __name__ == "__main__":
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main()
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import io
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def main():
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# Inject custom CSS to change the color of selected tasks
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st.markdown(
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"""
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<style>
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/* Change background color of selected items */
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.stMultiSelect [data-baseweb="tag"] {
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background-color: #3fa45bff !important; /* Custom green */
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color: white !important; /* White text */
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font-weight: medium;
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border-radius: 5px;
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padding: 5px 10px;
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}
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/* Change hover effect */
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.stMultiSelect [data-baseweb="tag"]:hover {
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background-color: #358d4d !important;
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}
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/* Style the dropdown input field */
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.stMultiSelect input {
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color: black !important;
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}
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unsafe_allow_html=True,
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)
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# Sidebar logo and title
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with st.sidebar:
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col1, col2 = st.columns([1, 5])
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with col1:
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logo = Image.open("logo.png")
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resized_logo = logo.resize((50, 50))
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st.image(resized_logo)
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with col2:
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st.markdown(
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"""
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<div style="
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display: flex;
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align-items: center;
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gap: 10px;
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margin: 0;
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padding: 0;
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font-family: 'Inter', sans-serif;
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font-size: 26px;
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font-weight: bold;">
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AI Energy Score
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</div>
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""",
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unsafe_allow_html=True,
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)
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st.sidebar.markdown("<hr style='border: 1px solid gray; margin: 15px 0;'>", unsafe_allow_html=True)
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st.sidebar.write("### Generate Label:")
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# Define the ordered list of tasks.
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task_order = [
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"Text Generation",
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"Image Generation",
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"Text Classification",
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"Image Classification",
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"Image Captioning",
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"Summarization",
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"Speech-to-Text (ASR)",
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"Object Detection",
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"Question Answering",
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"Sentence Similarity"
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]
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# Task selection
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st.sidebar.write("#### 1. Select task(s) to view models")
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selected_tasks = st.sidebar.multiselect("", options=task_order, default=["Text Generation"])
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# Mapping from task to CSV file name.
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task_to_file = {
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"Text Generation": "text_gen_energyscore.csv",
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"Image Generation": "image_generation_energyscore.csv",
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"Question Answering": "question_answering_energyscore.csv",
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"Sentence Similarity": "sentence_similarity_energyscore.csv"
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}
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st.sidebar.write("#### 2. Select a model to generate label")
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default_model_data = {
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'provider': "AI Provider",
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'energy': "?",
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'score': 5
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}
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if not selected_tasks:
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model_data = default_model_data
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else:
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df['full_model'] = df['model']
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df[['provider', 'model']] = df['model'].str.split(pat='/', n=1, expand=True)
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# Multiply raw energy by 1000 to convert to Wh, then round to 2 decimals
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df['energy'] = (df['total_gpu_energy'] * 1000).round(2)
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df['score'] = df['energy_score'].fillna(1).astype(int)
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df['date'] = "February 2025"
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df['hardware'] = "NVIDIA H100-80GB"
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model_data = data_df[data_df["full_model"] == selected_model].iloc[0]
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st.sidebar.write("#### 3. Download the label")
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try:
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score = int(model_data["score"])
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background_path = f"{score}.png"
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return
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final_size = (520, 728)
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generated_label = create_label_single_pass(background, model_data, final_size)
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st.image(generated_label, caption="Generated Label Preview", width=520)
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img_buffer = io.BytesIO()
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mime="image/png"
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)
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st.sidebar.write("#### 4. Share your label! [Guidelines](https://huggingface.github.io/AIEnergyScore/#labelusage)")
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st.sidebar.markdown("<hr style='border: 1px solid gray; margin: 15px 0;'>", unsafe_allow_html=True)
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st.sidebar.write("### Key Links")
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st.sidebar.markdown(
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"""
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<ul style="margin-top: 0; margin-bottom: 0; padding-left: 20px;">
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<li><a href="https://huggingface.co/spaces/AIEnergyScore/Leaderboard" target="_blank">Leaderboard</a></li>
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<li><a href="https://huggingface.co/spaces/AIEnergyScore/submission_portal" target="_blank">Submission Portal</a></li>
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<li><a href="https://huggingface.github.io/AIEnergyScore/#faq" target="_blank">FAQ</a></li>
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<li><a href="https://huggingface.github.io/AIEnergyScore/#documentation" target="_blank">Documentation</a></li>
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</ul>
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""",
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unsafe_allow_html=True,
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)
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def create_label_single_pass(background_image, model_data, final_size=(520, 728)):
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bg_resized = background_image.resize(final_size, Image.Resampling.LANCZOS)
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draw = ImageDraw.Draw(bg_resized)
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try:
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title_font = ImageFont.truetype("Inter_24pt-Bold.ttf", size=27)
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details_font = ImageFont.truetype("Inter_18pt-Regular.ttf", size=23)
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energy_font = ImageFont.truetype("Inter_18pt-Medium.ttf", size=24)
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except Exception as e:
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st.error(f"Font loading failed: {e}")
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return bg_resized
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title_x, title_y = 33, 150
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details_x, details_y = 480, 256
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energy_x = 480 # Right margin for the energy value
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energy_y = 472
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# Capitalize only the first letter of the first word while keeping the rest as is
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def smart_capitalize(text):
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"""Capitalizes the first letter of a string only if it's not already capitalized."""
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if not text:
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return text # Return unchanged if empty
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return text if text[0].isupper() else text[0].upper() + text[1:]
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# Apply smart capitalization
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provider_text = smart_capitalize(str(model_data['provider']))
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model_text = smart_capitalize(str(model_data['model']))
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draw.text((title_x, title_y), provider_text, font=title_font, fill="black")
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draw.text((title_x, title_y + 38), model_text, font=title_font, fill="black")
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details_lines = [str(model_data['date']), str(model_data['task']), str(model_data['hardware'])]
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for i, line in enumerate(details_lines):
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bbox = draw.textbbox((0, 0), line, font=details_font)
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text_width = bbox[2] - bbox[0] # Get text width
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draw.text((details_x - text_width, details_y + i * 47), line, font=details_font, fill="black")
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# Format the energy value to 2 decimal places and right-align the text
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energy_text = f"{model_data['energy']:.2f}"
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energy_bbox = draw.textbbox((0, 0), energy_text, font=energy_font)
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energy_text_width = energy_bbox[2] - energy_bbox[0]
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draw.text((energy_x - energy_text_width, energy_y), energy_text, font=energy_font, fill="black")
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return bg_resized
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if __name__ == "__main__":
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main()
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