Update app.py

app.py

@@ -8,7 +8,27 @@ def main():
     st.sidebar.image("logo.png", use_container_width=True)  # Display the logo at the top
     st.sidebar.title("Label Generator")
 
-    # Instructions with integrated dropdown
+    # Initialize data_df
+    data_df = None
+
+    # Read Data from CSV
+    try:
+        data_df = pd.read_csv("data.csv")
+    except FileNotFoundError:
+        st.sidebar.error("Could not find 'data.csv'! Please make sure it's present.")
+        return
+    except Exception as e:
+        st.sidebar.error(f"Error reading 'data.csv': {e}")
+        return
+
+    # 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
+
+    # Dropdown for selecting a model
     st.sidebar.write("### Instructions:")
     st.sidebar.write("1. Select a model from the dropdown below:")
     model_options = data_df["model"].unique().tolist()  # Get model options
@@ -18,9 +38,38 @@ def main():
 
     # Add step 2 instructions and move the Download button
     st.sidebar.write("2. Review the label preview and download your label below:")
+
+    # Dynamically select the background image and generate label (this part assumes data_df is valid)
+    model_data = data_df[data_df["model"] == selected_model].iloc[0]
+
+    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,
@@ -32,24 +81,6 @@ 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)")
 
-    # Read Data from CSV
-    try:
-        data_df = pd.read_csv("data.csv")
-    except FileNotFoundError:
-        st.sidebar.error("Could not find 'data.csv'! Please make sure it's present.")
-        return
-
-    # 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
-
-    # Dropdown for selecting a model
-    model_options = data_df["model"].unique().tolist()
-    selected_model = st.sidebar.selectbox("Select a Model:", model_options)
-
     # Filter the data for the selected model
     model_data = data_df[data_df["model"] == selected_model].iloc[0]