Update app.py

app.py

@@ -7,12 +7,12 @@ def main():
     # Sidebar logo and title
     with st.sidebar:
         col1, col2 = st.columns([1, 5])  # Shrink the logo column and expand the text column
-    
+
         with col1:
             logo = Image.open("logo.png")
             resized_logo = logo.resize((40, 40))  # Resize the logo
             st.image(resized_logo)
-    
+
         with col2:
             st.markdown(
                 """
@@ -31,24 +31,13 @@ def main():
                 unsafe_allow_html=True,
             )
 
-    # Sidebar instructions and link
-    st.sidebar.markdown(
-    """
-    <h1 style="text-align: center; font-size: 24px; font-weight: bold;">
-        Generate a Label to Display your 
-        <a href="https://huggingface.co/spaces/AIEnergyScore/Leaderboard" target="_blank" style="text-decoration: none; color: inherit;">
-            AI Energy Score
-        </a>
-    </h1>
-    """,
-    unsafe_allow_html=True,
-    )
+    # (Removed the "Generate a Label to Display your AI Energy Score" section)
 
     st.sidebar.markdown("<hr style='border: 1px solid gray; margin: 15px 0;'>", unsafe_allow_html=True)
 
-    st.sidebar.write("### Instructions:")
-    st.sidebar.write("#### 1. Select task(s)")
-    
+    # Update instructions header
+    st.sidebar.write("### Generate Label:")
+
     # Define the ordered list of tasks.
     task_order = [
         "Text Generation", 
@@ -62,15 +51,11 @@ def main():
         "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")
-    
+
+    # Make the task selection label green and remove redundant text.
+    st.sidebar.markdown('<p style="color: green; font-size: 16px;">Task(s):</p>', unsafe_allow_html=True)
+    selected_tasks = st.sidebar.multiselect("", options=task_order, default=task_order)
+
     # Mapping from task to CSV file name.
     task_to_file = {
         "Text Generation": "text_gen_energyscore.csv",
@@ -84,57 +69,71 @@ def main():
         "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(pat='/', n=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' (fill missing values with 1 to avoid casting errors)
-        df['score'] = df['energy_score'].fillna(1).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
-    
-    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
-
-    model_options = data_df["model"].unique().tolist()
-    selected_model = st.sidebar.selectbox(
-        "Scored Models",
-        model_options,
-        help="Start typing to search for a model"
-    )
-    
-    model_data = data_df[data_df["model"] == selected_model].iloc[0]
 
-    # Select background by score
+    # Default placeholder model data.
+    default_model_data = {
+         'provider': "AI Provider",
+         'model': "Model Name",
+         'full_model': "AI Provider/Model Name",
+         'date': "",
+         'task': "",
+         'hardware': "",
+         'energy': "?",
+         'score': 5
+    }
+
+    if not selected_tasks:
+        # If no tasks are selected, use generic placeholder.
+        model_data = default_model_data
+    else:
+        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
+
+            # Save the original full model string and then split the "model" column
+            df['full_model'] = df['model']
+            df[['provider', 'model']] = df['model'].str.split(pat='/', n=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' (fill missing values with 1 to avoid casting errors)
+            df['score'] = df['energy_score'].fillna(1).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:
+            model_data = default_model_data
+        else:
+            data_df = pd.concat(dfs, ignore_index=True)
+            if data_df.empty:
+                model_data = default_model_data
+            else:
+                # In the scored model dropdown show the full model string.
+                model_options = data_df["full_model"].unique().tolist()
+                selected_model = st.sidebar.selectbox(
+                    "Scored Models",
+                    model_options,
+                    help="Start typing to search for a model"
+                )
+                model_data = data_df[data_df["full_model"] == selected_model].iloc[0]
+
+    st.sidebar.write("#### 2. Select a model below")
+    st.sidebar.write("#### 3. Download the label")
+    
+    # Select background by score (using generic placeholder score=5 if applicable)
     try:
         score = int(model_data["score"])
         background_path = f"{score}.png"
@@ -163,14 +162,14 @@ def main():
         mime="image/png"
     )
 
-    st.sidebar.write("#### 3. Share your label! [Guidelines](https://huggingface.github.io/AIEnergyScore/#labelusage)")
+    st.sidebar.write("#### 4. Share your label! [Guidelines](https://huggingface.github.io/AIEnergyScore/#labelusage)")
     st.sidebar.markdown("<hr style='border: 1px solid gray; margin: 15px 0;'>", unsafe_allow_html=True)
     st.sidebar.write("### Key Links")
     st.sidebar.write("- [Leaderboard](https://huggingface.co/spaces/AIEnergyScore/Leaderboard)")
-    st.sidebar.write("- [Submission Portal](https://huggingface.co/spaces/AIEnergyScore/submission_portal)")    
+    st.sidebar.write("- [Submission Portal](https://huggingface.co/spaces/AIEnergyScore/submission_portal)")
     st.sidebar.write("- [FAQ](https://huggingface.github.io/AIEnergyScore/#faq)")
     st.sidebar.write("- [Documentation](https://huggingface.github.io/AIEnergyScore/#documentation)")
-    
+
 
 def create_label_single_pass(background_image, model_data, final_size=(520, 728)):
     """
@@ -189,16 +188,15 @@ def create_label_single_pass(background_image, model_data, final_size=(520, 728)
         st.error(f"Font loading failed: {e}")
         return bg_resized
 
-    # 3. Place your text. 
-    #    You may need to experiment with x/y coordinates or font sizes
-    #    to make it look right in 520×728.
+    # 3. Place your text.
+    # Flip the order so that the provider (AI Developer) is shown first and the model name second.
     title_x, title_y = 33, 150
     details_x, details_y = 480, 256
     energy_x, energy_y = 480, 472
 
-    # 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")
+    # Text 1 (title) – show provider first then model name
+    draw.text((title_x, title_y), str(model_data['provider']), font=title_font, fill="black")
+    draw.text((title_x, title_y + 38), str(model_data['model']), font=title_font, fill="black")
 
     # Text 2 (details)
     details_lines = [
@@ -210,7 +208,7 @@ def create_label_single_pass(background_image, model_data, final_size=(520, 728)
         bbox = draw.textbbox((0, 0), line, font=details_font)
         text_width = bbox[2] - bbox[0]
         # Right-justify the details text at details_x
-        draw.text((details_x - text_width, details_y + i*47), line, font=details_font, fill="black")
+        draw.text((details_x - text_width, details_y + i * 47), line, font=details_font, fill="black")
 
     # Text 3 (energy)
     energy_text = str(model_data['energy'])