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app.py

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+import streamlit as st
+import pandas as pd
+from PIL import Image, ImageDraw, ImageFont
+import io
+
+def main():
+    # 1. Page Title
+    st.title("AI Energy Score Label Generator")
+
+    # 2. Read Data from CSV
+    # Using pandas to read the 'data.csv' file
+    # Make sure 'data.csv' is in the same folder as 'app.py'
+    try:
+        data_df = pd.read_csv("data.csv")
+    except FileNotFoundError:
+        st.error("Could not find 'data.csv'! Please make sure it's present.")
+        return
+
+    # 3. Ensure the CSV has a "Model" column
+    if "Model" not in data_df.columns:
+        st.error("The CSV file must contain a column named 'Model'.")
+        return
+
+    # 4. Create a dropdown list based on unique values in the 'Model' column
+    model_options = data_df["Model"].unique().tolist()
+    selected_model = st.selectbox("Select a Model:", model_options)
+
+    # 5. Filter the data for the selected model
+    model_data = data_df[data_df["Model"] == selected_model].iloc[0]
+
+    # 6. Load the background image for the label
+    # Make sure 'background.png' is in the same folder as 'app.py'
+    try:
+        background = Image.open("background.png")
+    except FileNotFoundError:
+        st.error("Could not find 'background.png'! Please make sure it's present.")
+        return
+
+    # 7. Overlay the data on the image
+    #    We'll create a function to do this cleanly.
+    generated_label = create_label(background, model_data)
+
+    # 8. Display the generated label in the Streamlit app
+    st.image(generated_label, caption="Generated Label Preview")
+
+    # 9. Provide a download button
+    #    We'll create an in-memory file to let user download the image.
+    img_buffer = io.BytesIO()
+    generated_label.save(img_buffer, format="PNG")
+    img_buffer.seek(0)
+
+    st.download_button(
+        label="Download Label as PNG",
+        data=img_buffer,
+        file_name="AIEnergyScore.png",
+        mime="image/png"
+    )
+
+def create_label(background_image, model_data):
+    """
+    This function takes a background image and a row (model_data) from the CSV,
+    then draws text on the image. Finally, returns the modified image object.
+    """
+    # Convert background to a format that can be edited (RGBA mode).
+    label_img = background_image.convert("RGBA")
+
+    # Create a Drawing context
+    draw = ImageDraw.Draw(label_img)
+
+    # Choose a font and size. Change path/size as needed.
+    # If you don't have a custom font file, you can use a PIL built-in font.
+    try:
+        font = ImageFont.truetype("Roboto-Regular.ttf", 20)  # or "Arial.ttf", etc.
+    except:
+        # Fallback to a default PIL font if 'arial.ttf' is not found
+        font = ImageFont.load_default()
+
+    # You can customize the positions, colors, etc. For instance:
+    # We'll just place the text in a simple stacked format at a fixed position.
+    # Position variables (x, y) - Adjust as needed.
+    x_position = 50
+    y_position = 50
+    line_spacing = 30
+
+    # Extracting data from the row. Customize these lines based on your CSV columns.
+    model_name = f"Model: {model_data['Model']}"
+    if 'Task' in model_data:
+        task = f"Task: {model_data['Task']}"
+    else:
+        task = "Task: N/A"
+    if 'date' in model_data:
+        date = f"Date: {model_data['Date']}"
+    else:
+        date = "Date: N/A"
+    if 'Energy' in model_data:
+        energy = f"Energy: {model_data['Energy']}"
+    else:
+        energy = "Energy: N/A"
+
+    text_lines = [model_name, task, date, energy]
+
+    # Draw each line on the image
+    for line in text_lines:
+        draw.text((x_position, y_position), line, fill="black", font=font)
+        y_position += line_spacing
+
+    return label_img
+
+if __name__ == "__main__":
+    main()

data.csv

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+Model,Task,Date,Energy
+GPT2,text generation,Feb-25,122
+SmolLm,text generation,Feb-25,1.23
+Llama3.2,text generation,Feb-25,13213

requirements.txt

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+streamlit
+pandas
+Pillow