Update app.py

app.py

@@ -46,34 +46,92 @@ def main():
 
     st.sidebar.markdown("<hr style='border: 1px solid gray; margin: 15px 0;'>", unsafe_allow_html=True)
 
-    # Load 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}")
+    st.sidebar.write("### Instructions:")
+    st.sidebar.write("#### 1. Select task(s)")
+    
+    # Define the ordered list of tasks.
+    task_order = [
+        "Text Generation", 
+        "Image Generation", 
+        "Text Classification", 
+        "Image Classification", 
+        "Image Captioning", 
+        "Summarization", 
+        "ASR", 
+        "Object Detection", 
+        "Question Answering", 
+        "Sentence Similarity"
+    ]
+    
+    # Multi-select dropdown for tasks.
+    selected_tasks = st.sidebar.multiselect("Select Task(s)", options=task_order, default=task_order)
+    if not selected_tasks:
+        st.sidebar.error("Please select at least one task.")
+        st.stop()
+    
+    st.sidebar.write("#### 2. Select a model below")
+    
+    # Mapping from task to CSV file name.
+    task_to_file = {
+        "Text Generation": "text_gen_energyscore.csv",
+        "Image Generation": "image_generation_energyscore.csv",
+        "Text Classification": "text_classification_energyscore.csv",
+        "Image Classification": "image_classification_energyscore.csv",
+        "Image Captioning": "image_caption_energyscore.csv",
+        "Summarization": "summarization_energyscore.csv",
+        "ASR": "asr_energyscore.csv",
+        "Object Detection": "object_detection_energyscore.csv",
+        "Question Answering": "question_answering_energyscore.csv",
+        "Sentence Similarity": "sentence_similarity_energyscore.csv"
+    }
+    
+    dfs = []
+    # Load and process each CSV corresponding to the selected tasks.
+    for task in selected_tasks:
+        file_name = task_to_file[task]
+        try:
+            df = pd.read_csv(file_name)
+        except FileNotFoundError:
+            st.sidebar.error(f"Could not find '{file_name}' for task {task}!")
+            continue
+        except Exception as e:
+            st.sidebar.error(f"Error reading '{file_name}' for task {task}: {e}")
+            continue
+        
+        # Split the "Model" column into 'provider' (before the "/") and 'model' (after the "/")
+        df[['provider', 'model']] = df['Model'].str.split('/', 1, expand=True)
+        # Round total_gpu_energy to 3 decimal places and assign to 'energy'
+        df['energy'] = df['total_gpu_energy'].round(3)
+        # Use the energy_score column as 'score'
+        df['score'] = df['energy_score'].astype(int)
+        # Hardcode date and hardware
+        df['date'] = "February 2025"
+        df['hardware'] = "NVIDIA H100-80GB"
+        # Set the task from the file name mapping
+        df['task'] = task
+        
+        dfs.append(df)
+    
+    if not dfs:
+        st.sidebar.error("No data available for the selected task(s).")
         return
-
-    # Check columns
+    
+    data_df = pd.concat(dfs, ignore_index=True)
+    
+    # Check 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
 
-    st.sidebar.write("### Instructions:")
-    st.sidebar.write("#### 1. Select a model below")
-
     model_options = data_df["model"].unique().tolist()
     selected_model = st.sidebar.selectbox(
         "Scored Models",
         model_options,
         help="Start typing to search for a model"
     )
-    st.sidebar.write("#### 2. Download the label")
-
+    
     model_data = data_df[data_df["model"] == selected_model].iloc[0]
 
     # Select background by score
@@ -138,7 +196,7 @@ def create_label_single_pass(background_image, model_data, final_size=(520, 728)
     details_x, details_y = 480, 256
     energy_x, energy_y = 480, 472
 
-    # Text 1 (title)
+    # Text 1 (title) - model and provider separated on different lines
     draw.text((title_x, title_y), str(model_data['model']), font=title_font, fill="black")
     draw.text((title_x, title_y + 38), str(model_data['provider']), font=title_font, fill="black")