Update app.py

app.py

@@ -4,52 +4,59 @@ from PIL import Image, ImageDraw, ImageFont
 import io
 
 def main():
-    # 1. Page Title
-    st.title("AI Energy Score Label Generator")
+    # 1. Sidebar for Dropdown, Buttons, and Instructions
+    st.sidebar.title("AI Energy Score Label Generator")
+    st.sidebar.write("### Instructions:")
+    st.sidebar.write("1. Select a model from the dropdown.")
+    st.sidebar.write("2. Review the label preview.")
+    st.sidebar.write("3. Download the label as a PNG.")
+    st.sidebar.markdown("[Learn more about AI Energy Scores](https://example.com)")
 
     # 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.")
+        st.sidebar.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
+    # 3. Ensure the CSV has required columns
+    required_columns = ["model", "provider", "date", "task", "hardware", "energy", "score"]
+    for col in required_columns:
+        if col not in data_df.columns:
+            st.sidebar.error(f"The CSV file must contain a column named '{col}'.")
+            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)
+    # 4. Create a dropdown list based on unique values in the 'model' column
+    model_options = data_df["model"].unique().tolist()
+    selected_model = st.sidebar.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]
+    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'
+    # 6. Dynamically select the background image based on the score
     try:
-        background = Image.open("background.png")
+        score = int(model_data["score"])  # Convert to int to avoid issues
+        background_path = f"{score}.png"  # E.g., "1.png", "2.png"
+        background = Image.open(background_path).convert("RGBA")
     except FileNotFoundError:
-        st.error("Could not find 'background.png'! Please make sure it's present.")
+        st.sidebar.error(f"Could not find background image '{score}.png'. Using default background.")
+        background = Image.open("default_background.png").convert("RGBA")
+    except ValueError:
+        st.sidebar.error(f"Invalid score '{model_data['score']}'. Score must be an integer.")
         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
+    # 8. Display the generated label in the main area
     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.
+    # 9. Provide a download button in the sidebar
     img_buffer = io.BytesIO()
     generated_label.save(img_buffer, format="PNG")
     img_buffer.seek(0)
 
-    st.download_button(
+    st.sidebar.download_button(
         label="Download Label as PNG",
         data=img_buffer,
         file_name="AIEnergyScore.png",
@@ -61,49 +68,40 @@ 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.
+    # Load the Inter variable font
     try:
-        font = ImageFont.truetype("Roboto-SemiBold.ttf", 30)
-        print("Font loaded successfully!")
+        inter_font_path = "Inter-VariableFont_opsz,wght.ttf"
+        title_font = ImageFont.truetype(inter_font_path, 16, layout_engine=ImageFont.LAYOUT_RAQM)
+        details_font = ImageFont.truetype(inter_font_path, 12, layout_engine=ImageFont.LAYOUT_RAQM)
+        energy_font = ImageFont.truetype(inter_font_path, 14, layout_engine=ImageFont.LAYOUT_RAQM)
     except Exception as e:
-        font = ImageFont.load_default()
-        print(f"Font loading failed: {e}")
-
-    # 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 = 75
-    y_position = 350
-    line_spacing = 50
-
-    # 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
+        st.error(f"Font loading failed: {e}")
+        return label_img
+
+    # Define positions for the text groups (easy to tweak!)
+    title_x, title_y = 20, 20  # Position for title group
+    details_x, details_y = 300, 20  # Position for details group
+    energy_x, energy_y = 150, 400  # Position for energy group
+
+    # Group 1: Title (Left-Justified)
+    draw.text((title_x, title_y), f"Model: {model_data['model']}", font=title_font, fill="black")
+    draw.text((title_x, title_y + 25), f"Provider: {model_data['provider']}", font=title_font, fill="black")
+
+    # Group 2: Details (Right-Justified)
+    details_lines = [
+        f"Date: {model_data['date']}",
+        f"Task: {model_data['task']}",
+        f"Hardware: {model_data['hardware']}"
+    ]
+    for i, line in enumerate(details_lines):
+        draw.text((details_x, details_y + i * 20), line, font=details_font, fill="black", anchor="ra")
+
+    # Group 3: Energy (Bottom-Center)
+    energy_text = f"Energy: {model_data['energy']}"
+    draw.text((energy_x, energy_y), energy_text, font=energy_font, fill="black")
 
     return label_img