Spaces:
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Running
Update app.py
Browse files
app.py
CHANGED
@@ -7,12 +7,12 @@ def main():
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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]) # Shrink the logo column and expand the text column
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-
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with col1:
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logo = Image.open("logo.png")
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resized_logo = logo.resize((40, 40)) # Resize the logo
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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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@@ -31,24 +31,13 @@ def main():
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unsafe_allow_html=True,
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)
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#
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st.sidebar.markdown(
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"""
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<h1 style="text-align: center; font-size: 24px; font-weight: bold;">
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Generate a Label to Display your
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<a href="https://huggingface.co/spaces/AIEnergyScore/Leaderboard" target="_blank" style="text-decoration: none; color: inherit;">
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AI Energy Score
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</a>
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</h1>
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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("
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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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@@ -62,15 +51,11 @@ def main():
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"Question Answering",
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"Sentence Similarity"
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]
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#
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st.stop()
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st.sidebar.write("#### 2. Select a model below")
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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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@@ -84,57 +69,71 @@ 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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dfs = []
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# Load and process each CSV corresponding to the selected tasks.
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for task in selected_tasks:
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file_name = task_to_file[task]
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try:
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df = pd.read_csv(file_name)
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except FileNotFoundError:
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st.sidebar.error(f"Could not find '{file_name}' for task {task}!")
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continue
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except Exception as e:
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st.sidebar.error(f"Error reading '{file_name}' for task {task}: {e}")
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continue
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# Split the "model" column into 'provider' (before the "/") and 'model' (after the "/")
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df[['provider', 'model']] = df['model'].str.split(pat='/', n=1, expand=True)
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# Round total_gpu_energy to 3 decimal places and assign to 'energy'
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df['energy'] = df['total_gpu_energy'].round(3)
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# Use the energy_score column as 'score' (fill missing values with 1 to avoid casting errors)
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df['score'] = df['energy_score'].fillna(1).astype(int)
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# Hardcode date and hardware
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df['date'] = "February 2025"
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df['hardware'] = "NVIDIA H100-80GB"
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# Set the task from the file name mapping
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df['task'] = task
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dfs.append(df)
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if not dfs:
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st.sidebar.error("No data available for the selected task(s).")
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return
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data_df = pd.concat(dfs, ignore_index=True)
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# Check required columns
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required_columns = ["model", "provider", "date", "task", "hardware", "energy", "score"]
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for col in required_columns:
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if col not in data_df.columns:
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st.sidebar.error(f"The CSV file must contain a column named '{col}'.")
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return
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model_options = data_df["model"].unique().tolist()
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selected_model = st.sidebar.selectbox(
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"Scored Models",
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model_options,
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help="Start typing to search for a model"
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)
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model_data = data_df[data_df["model"] == selected_model].iloc[0]
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#
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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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@@ -163,14 +162,14 @@ def main():
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mime="image/png"
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)
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st.sidebar.write("####
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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.write("- [Leaderboard](https://huggingface.co/spaces/AIEnergyScore/Leaderboard)")
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st.sidebar.write("- [Submission Portal](https://huggingface.co/spaces/AIEnergyScore/submission_portal)")
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st.sidebar.write("- [FAQ](https://huggingface.github.io/AIEnergyScore/#faq)")
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st.sidebar.write("- [Documentation](https://huggingface.github.io/AIEnergyScore/#documentation)")
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def create_label_single_pass(background_image, model_data, final_size=(520, 728)):
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"""
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@@ -189,16 +188,15 @@ def create_label_single_pass(background_image, model_data, final_size=(520, 728)
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st.error(f"Font loading failed: {e}")
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return bg_resized
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# 3. Place your text.
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#
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# to make it look right in 520×728.
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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, energy_y = 480, 472
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# Text 1 (title)
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draw.text((title_x, title_y), str(model_data['
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draw.text((title_x, title_y + 38), str(model_data['
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# Text 2 (details)
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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]
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# Right-justify the details text at details_x
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draw.text((details_x - text_width, details_y + i*47), line, font=details_font, fill="black")
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# Text 3 (energy)
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energy_text = str(model_data['energy'])
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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]) # Shrink the logo column and expand the text column
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with col1:
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logo = Image.open("logo.png")
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resized_logo = logo.resize((40, 40)) # Resize the logo
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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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unsafe_allow_html=True,
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)
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# (Removed the "Generate a Label to Display your AI Energy Score" section)
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st.sidebar.markdown("<hr style='border: 1px solid gray; margin: 15px 0;'>", unsafe_allow_html=True)
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# Update instructions header
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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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"Question Answering",
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"Sentence Similarity"
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]
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# Make the task selection label green and remove redundant text.
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st.sidebar.markdown('<p style="color: green; font-size: 16px;">Task(s):</p>', unsafe_allow_html=True)
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selected_tasks = st.sidebar.multiselect("", options=task_order, default=task_order)
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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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"Question Answering": "question_answering_energyscore.csv",
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"Sentence Similarity": "sentence_similarity_energyscore.csv"
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}
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# Default placeholder model data.
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default_model_data = {
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'provider': "AI Provider",
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'model': "Model Name",
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'full_model': "AI Provider/Model Name",
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'date': "",
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'task': "",
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'hardware': "",
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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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# If no tasks are selected, use generic placeholder.
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model_data = default_model_data
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else:
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dfs = []
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# Load and process each CSV corresponding to the selected tasks.
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for task in selected_tasks:
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file_name = task_to_file[task]
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try:
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df = pd.read_csv(file_name)
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except FileNotFoundError:
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st.sidebar.error(f"Could not find '{file_name}' for task {task}!")
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continue
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except Exception as e:
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st.sidebar.error(f"Error reading '{file_name}' for task {task}: {e}")
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continue
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# Save the original full model string and then split the "model" column
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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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# Round total_gpu_energy to 3 decimal places and assign to 'energy'
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df['energy'] = df['total_gpu_energy'].round(3)
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# Use the energy_score column as 'score' (fill missing values with 1 to avoid casting errors)
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df['score'] = df['energy_score'].fillna(1).astype(int)
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# Hardcode date and hardware
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df['date'] = "February 2025"
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df['hardware'] = "NVIDIA H100-80GB"
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# Set the task from the file name mapping
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df['task'] = task
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dfs.append(df)
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if not dfs:
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model_data = default_model_data
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else:
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data_df = pd.concat(dfs, ignore_index=True)
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if data_df.empty:
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model_data = default_model_data
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else:
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# In the scored model dropdown show the full model string.
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model_options = data_df["full_model"].unique().tolist()
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selected_model = st.sidebar.selectbox(
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"Scored Models",
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model_options,
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help="Start typing to search for a model"
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)
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model_data = data_df[data_df["full_model"] == selected_model].iloc[0]
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st.sidebar.write("#### 2. Select a model below")
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st.sidebar.write("#### 3. Download the label")
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# Select background by score (using generic placeholder score=5 if applicable)
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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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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.write("- [Leaderboard](https://huggingface.co/spaces/AIEnergyScore/Leaderboard)")
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st.sidebar.write("- [Submission Portal](https://huggingface.co/spaces/AIEnergyScore/submission_portal)")
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st.sidebar.write("- [FAQ](https://huggingface.github.io/AIEnergyScore/#faq)")
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st.sidebar.write("- [Documentation](https://huggingface.github.io/AIEnergyScore/#documentation)")
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def create_label_single_pass(background_image, model_data, final_size=(520, 728)):
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"""
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st.error(f"Font loading failed: {e}")
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return bg_resized
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# 3. Place your text.
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# Flip the order so that the provider (AI Developer) is shown first and the model name second.
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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, energy_y = 480, 472
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# Text 1 (title) – show provider first then model name
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draw.text((title_x, title_y), str(model_data['provider']), font=title_font, fill="black")
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draw.text((title_x, title_y + 38), str(model_data['model']), font=title_font, fill="black")
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# Text 2 (details)
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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]
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# Right-justify the details text at details_x
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draw.text((details_x - text_width, details_y + i * 47), line, font=details_font, fill="black")
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# Text 3 (energy)
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energy_text = str(model_data['energy'])
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