Update app.py

app.py

@@ -30,18 +30,19 @@ def main():
 
     # Dropdown for selecting a model
     st.sidebar.write("### Instructions:")
-    st.sidebar.write("1. Select a model from the dropdown below:")
+    st.sidebar.write("1. Select a model:")
     model_options = data_df["model"].unique().tolist()  # Get model options
     selected_model = st.sidebar.selectbox(
-        "Select a Model", model_options, help="Start typing to search for a model"
+        "Scored Models", model_options, help="Start typing to search for a model"
     )  # Searchable dropdown
 
     # Add step 2 instructions and move the Download button
-    st.sidebar.write("2. Review the label preview and download your label below:")
+    st.sidebar.write("2. Download the label:")
 
-    # Dynamically select the background image and generate label (this part assumes data_df is valid)
+    # Filter the data for the selected model
     model_data = data_df[data_df["model"] == selected_model].iloc[0]
 
+    # Dynamically select the background image based on the score
     try:
         score = int(model_data["score"])  # Convert to int
         background_path = f"{score}.png"  # E.g., "1.png", "2.png"
@@ -71,7 +72,7 @@ def main():
     img_buffer.seek(0)
 
     st.sidebar.download_button(
-        label="Download Label as PNG",
+        label="Download Label",
         data=img_buffer,
         file_name="AIEnergyScore.png",
         mime="image/png"
@@ -81,45 +82,6 @@ def main():
     st.sidebar.write("3. Share your label in technical reports, announcements, etc.")
     st.sidebar.markdown("[AI Energy Score Leaderboard](https://huggingface.co/spaces/AIEnergyScore/Leaderboard)")
 
-    # Filter the data for the selected model
-    model_data = data_df[data_df["model"] == selected_model].iloc[0]
-
-    # Dynamically select the background image based on the score
-    try:
-        score = int(model_data["score"])  # Convert to int
-        background_path = f"{score}.png"  # E.g., "1.png", "2.png"
-        background = Image.open(background_path).convert("RGBA")
-
-        # Proportional scaling to fit within the target size
-        target_size = (800, 600)  # Maximum width and height
-        background.thumbnail(target_size, Image.Resampling.LANCZOS)
-
-    except FileNotFoundError:
-        st.sidebar.error(f"Could not find background image '{score}.png'. Using default background.")
-        background = Image.open("default_background.png").convert("RGBA")
-        background.thumbnail(target_size, Image.Resampling.LANCZOS)  # Resize default image proportionally
-    except ValueError:
-        st.sidebar.error(f"Invalid score '{model_data['score']}'. Score must be an integer.")
-        return
-
-    # Generate the label with text
-    generated_label = create_label(background, model_data)
-
-    # Display the label
-    st.image(generated_label, caption="Generated Label Preview")
-
-    # Download button for the label
-    img_buffer = io.BytesIO()
-    generated_label.save(img_buffer, format="PNG")
-    img_buffer.seek(0)
-
-    st.sidebar.download_button(
-        label="Download Label as PNG",
-        data=img_buffer,
-        file_name="AIEnergyScore.png",
-        mime="image/png"
-    )
-
 def create_label(background_image, model_data):
     """
     Create the label image by adding text from model_data to the background image.