Update app.py

app.py

@@ -1,196 +1,197 @@
-from utils import (
-    update_leaderboard_multilingual, 
-    update_leaderboard_one_vs_all,
-    handle_evaluation,
-    process_results_file,
-    create_html_image,
-)
-import os
-import gradio as gr
-
-from constants import *
-
-if __name__ == "__main__":
-    
-    with gr.Blocks() as app:
-               
-        base_path = os.path.dirname(__file__)
-        local_image_path = os.path.join(base_path, 'open_arabic_lid_arena.png')
-        
-        gr.HTML(create_html_image(local_image_path))
-        gr.Markdown("# 🏅 Open Arabic Dialect Identification Leaderboard")
-        
-        # Multi-dialects leaderboard
-        with gr.Tab("Multi-dialects model leaderboard"):
-            gr.Markdown("""
-                        Complete leaderboard across multiple arabic dialects. 
-                        Compare the performance of different models across various metrics such as FNR, FPR, and other clasical metrics.
-                        """
-            )
-            
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("### Select country to display")
-                    country_selector = gr.Dropdown(
-                        choices=supported_dialects,
-                        value='Morocco', # Default to Morocco of course
-                        label="Country"
-                    )
-                
-                with gr.Column(scale=2):
-                    gr.Markdown("### Select metrics to display")
-                    metric_checkboxes = gr.CheckboxGroup(
-                        choices=metrics,
-                        value=default_metrics,
-                        label="Metrics"
-                    )
-            
-            with gr.Row():
-                leaderboard_table = gr.DataFrame(
-                    interactive=False
-                )
-
-            gr.Markdown("</br>")
-            
-            gr.Markdown("## Contribute to the Leaderboard")
-            gr.Markdown("""
-                        We welcome contributions from the community! 
-                        If you have a model that you would like to see on the leaderboard, please use the 'Evaluate a model' or 'Upload your results' tabs to submit your model's performance.
-                        Let's work together to improve Arabic dialect identification! 🚀
-                        """)
-            
-        # Dialect confusion leaderboard
-        with gr.Tab("Dialect confusion leaderboard"): # use to be "One-vs-All leaderboard"
-            
-            gr.Markdown("""
-                        Detailed analysis of how well models distinguish specific dialects from others.
-                        For each target dialect, see how often models incorrectly classify other dialects as the target.
-                        Lower `false_positive_rate` indicate better ability to identify the true dialect by
-                        showing **how often it misclassifies other dialects as the target dialect**.
-                        """
-            )
-            
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.Markdown("### Select your target language")
-                    target_language_selector = gr.Dropdown(
-                        choices=languages_to_display_one_vs_all,
-                        value='Morocco', # Default to Morocco of course
-                        label="Target Language"
-                    )
-                
-                with gr.Column(scale=2):
-                    gr.Markdown("### Select languages to compare to")
-                    languages_checkboxes = gr.CheckboxGroup(
-                        choices=languages_to_display_one_vs_all,
-                        value=default_languages,
-                        label="Languages"
-                    )
-                
-            with gr.Row():
-                binary_leaderboard_table = gr.DataFrame(
-                    interactive=False
-                )
-            
-
-        with gr.Tab("Evaluate a model"):
-            gr.Markdown("Suggest a model to evaluate 🤗 (Supports only **Fasttext** models as SfayaLID, GlotLID, OpenLID, etc.)")
-            gr.Markdown("For other models, you are welcome to **submit your results** through the upload section.")
-            
-            model_path = gr.Textbox(label="Model Path", placeholder='path/to/model')
-            model_path_bin = gr.Textbox(label=".bin filename", placeholder='model.bin')
-            gr.Markdown("### **⚠️ To ensure correct results, tick this when the model's labels are the iso_codes**")
-            use_mapping = gr.Checkbox(label="Does not map to country", value=True)      # Initially enabled
-            eval_button = gr.Button("Evaluate", value=False)                            # Initially disabled
-            
-            # Status message area
-            status_message = gr.Markdown(value="")
-            def update_status_message():
-                return "### **⚠️Evaluating... Please wait...**"
-            
-            eval_button.click(update_status_message, outputs=[status_message])
-            
-            eval_button.click(handle_evaluation, inputs=[model_path, model_path_bin, use_mapping], outputs=[leaderboard_table, status_message])
-
-        with gr.Tab("Upload your results"):
-
-            # Define a code block to display
-            code_snippet = """ 
-            ```python
-            
-            # Load your model
-            model = ... # Load your model here
-            
-            # Load evaluation benchmark
-            eval_dataset = load_dataset("atlasia/Arabic-LID-Leaderboard", split='test').to_pandas() # do not change this line :)
-            
-            # Predict labels using your model
-            eval_dataset['preds'] = eval_dataset['text'].apply(lambda text: predict_label(text, model)) # predict_label is a function that you need to define for your model
-            
-            # now drop the columns that are not needed, i.e. 'text', 'metadata' and 'dataset_source'
-            df_eval = df_eval.drop(columns=['text', 'metadata', 'dataset_source'])
-            df_eval.to_csv('your_model_name.csv')
-            
-            # submit your results: 'your_model_name.csv' to the leaderboard
-            
-            ```
-            """
-            
-            gr.Markdown("## Upload your results to the leaderboard 🚀")
-            gr.Markdown("### Submission guidelines: Run the test dataset on your model and save the results in a CSV file. Bellow a code snippet to help you with that.")
-            gr.Markdown("### Nota Bene: The One-vs-All leaderboard evaluation is currently unavailable with the csv upload but will be implemented soon. Stay tuned!")
-            gr.Markdown(code_snippet)
-
-            uploaded_model_name = gr.Textbox(label="Model name", placeholder='Your model/team name')
-            file = gr.File(label="Upload your results")
-            upload_button = gr.Button("Upload")
-            
-            # Status message area
-            status_message = gr.Markdown(value="")
-            def update_status_message():
-                return "### **⚠️Evaluating... Please wait...**"
-            
-            upload_button.click(update_status_message, outputs=[status_message])
-            
-            upload_button.click(process_results_file, inputs=[file, uploaded_model_name], outputs=[leaderboard_table, status_message])
-        
-        # Update multilangual table when any input changes
-        country_selector.change(
-            update_leaderboard_multilingual,
-            inputs=[country_selector, metric_checkboxes],
-            outputs=leaderboard_table
-        )
-        
-        metric_checkboxes.change(
-            update_leaderboard_multilingual,
-            inputs=[country_selector, metric_checkboxes],
-            outputs=leaderboard_table
-        )
-        
-        # Update binary table when any input changes
-        target_language_selector.change(
-            update_leaderboard_one_vs_all,
-            inputs=[target_language_selector, languages_checkboxes],
-            outputs=[binary_leaderboard_table, languages_checkboxes]
-        )
-        
-        languages_checkboxes.change(
-            update_leaderboard_one_vs_all,
-            inputs=[target_language_selector, languages_checkboxes],
-            outputs=[binary_leaderboard_table, languages_checkboxes]
-        )
-        
-        # Define load event to run at startup
-        app.load(
-            update_leaderboard_one_vs_all,
-            inputs=[target_language_selector, languages_checkboxes],
-            outputs=[binary_leaderboard_table, languages_checkboxes]
-        )
-        
-        app.load(
-            update_leaderboard_multilingual,
-            inputs=[country_selector, metric_checkboxes],
-            outputs=leaderboard_table
-        )
-        
-    app.launch(allowed_paths=[base_path])
+from utils import (
+    update_leaderboard_multilingual, 
+    update_leaderboard_one_vs_all,
+    handle_evaluation,
+    process_results_file,
+    create_html_image,
+)
+
+import os
+import gradio as gr
+
+from constants import *
+
+if __name__ == "__main__":
+    
+    with gr.Blocks() as app:
+               
+        base_path = os.path.dirname(__file__)
+        local_image_path = os.path.join(base_path, 'open_arabic_lid_arena.png')
+        
+        gr.HTML(create_html_image(local_image_path))
+        gr.Markdown("# 🏅 Open Arabic Dialect Identification Leaderboard")
+        
+        # Multi-dialects leaderboard
+        with gr.Tab("Multi-dialects model leaderboard"):
+            gr.Markdown("""
+                        Complete leaderboard across multiple arabic dialects. 
+                        Compare the performance of different models across various metrics such as FNR, FPR, and other clasical metrics.
+                        """
+            )
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.Markdown("### Select country to display")
+                    country_selector = gr.Dropdown(
+                        choices=supported_dialects,
+                        value='Morocco', # Default to Morocco of course
+                        label="Country"
+                    )
+                
+                with gr.Column(scale=2):
+                    gr.Markdown("### Select metrics to display")
+                    metric_checkboxes = gr.CheckboxGroup(
+                        choices=metrics,
+                        value=default_metrics,
+                        label="Metrics"
+                    )
+            
+            with gr.Row():
+                leaderboard_table = gr.DataFrame(
+                    interactive=False
+                )
+
+            gr.Markdown("</br>")
+            
+            gr.Markdown("## Contribute to the Leaderboard")
+            gr.Markdown("""
+                        We welcome contributions from the community! 
+                        If you have a model that you would like to see on the leaderboard, please use the 'Evaluate a model' or 'Upload your results' tabs to submit your model's performance.
+                        Let's work together to improve Arabic dialect identification! 🚀
+                        """)
+            
+        # Dialect confusion leaderboard
+        with gr.Tab("Dialect confusion leaderboard"): # use to be "One-vs-All leaderboard"
+            
+            gr.Markdown("""
+                        Detailed analysis of how well models distinguish specific dialects from others.
+                        For each target dialect, see how often models incorrectly classify other dialects as the target.
+                        Lower `false_positive_rate` indicate better ability to identify the true dialect by
+                        showing **how often it misclassifies other dialects as the target dialect**.
+                        """
+            )
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.Markdown("### Select your target language")
+                    target_language_selector = gr.Dropdown(
+                        choices=languages_to_display_one_vs_all,
+                        value='Morocco', # Default to Morocco of course
+                        label="Target Language"
+                    )
+                
+                with gr.Column(scale=2):
+                    gr.Markdown("### Select languages to compare to")
+                    languages_checkboxes = gr.CheckboxGroup(
+                        choices=languages_to_display_one_vs_all,
+                        value=default_languages,
+                        label="Languages"
+                    )
+                
+            with gr.Row():
+                binary_leaderboard_table = gr.DataFrame(
+                    interactive=False
+                )
+            
+
+        with gr.Tab("Evaluate a model"):
+            gr.Markdown("Suggest a model to evaluate 🤗 (Supports only **Fasttext** models as SfayaLID, GlotLID, OpenLID, etc.)")
+            gr.Markdown("For other models, you are welcome to **submit your results** through the upload section.")
+            
+            model_path = gr.Textbox(label="Model Path", placeholder='path/to/model')
+            model_path_bin = gr.Textbox(label=".bin filename", placeholder='model.bin')
+            gr.Markdown("### **⚠️ To ensure correct results, tick this when the model's labels are the iso_codes**")
+            use_mapping = gr.Checkbox(label="Does not map to country", value=True)      # Initially enabled
+            eval_button = gr.Button("Evaluate", value=False)                            # Initially disabled
+            
+            # Status message area
+            status_message = gr.Markdown(value="")
+            def update_status_message():
+                return "### **⚠️Evaluating... Please wait...**"
+            
+            eval_button.click(update_status_message, outputs=[status_message])
+            
+            eval_button.click(handle_evaluation, inputs=[model_path, model_path_bin, use_mapping], outputs=[leaderboard_table, status_message])
+
+        with gr.Tab("Upload your results"):
+
+            # Define a code block to display
+            code_snippet = """ 
+            ```python
+            
+            # Load your model
+            model = ... # Load your model here
+            
+            # Load evaluation benchmark
+            eval_dataset = load_dataset("atlasia/Arabic-LID-Leaderboard", split='test').to_pandas() # do not change this line :)
+            
+            # Predict labels using your model
+            eval_dataset['preds'] = eval_dataset['text'].apply(lambda text: predict_label(text, model)) # predict_label is a function that you need to define for your model
+            
+            # now drop the columns that are not needed, i.e. 'text', 'metadata' and 'dataset_source'
+            df_eval = df_eval.drop(columns=['text', 'metadata', 'dataset_source'])
+            df_eval.to_csv('your_model_name.csv')
+            
+            # submit your results: 'your_model_name.csv' to the leaderboard
+            
+            ```
+            """
+            
+            gr.Markdown("## Upload your results to the leaderboard 🚀")
+            gr.Markdown("### Submission guidelines: Run the test dataset on your model and save the results in a CSV file. Bellow a code snippet to help you with that.")
+            gr.Markdown("### Nota Bene: The One-vs-All leaderboard evaluation is currently unavailable with the csv upload but will be implemented soon. Stay tuned!")
+            gr.Markdown(code_snippet)
+
+            uploaded_model_name = gr.Textbox(label="Model name", placeholder='Your model/team name')
+            file = gr.File(label="Upload your results")
+            upload_button = gr.Button("Upload")
+            
+            # Status message area
+            status_message = gr.Markdown(value="")
+            def update_status_message():
+                return "### **⚠️Evaluating... Please wait...**"
+            
+            upload_button.click(update_status_message, outputs=[status_message])
+            
+            upload_button.click(process_results_file, inputs=[file, uploaded_model_name], outputs=[leaderboard_table, status_message])
+        
+        # Update multilangual table when any input changes
+        country_selector.change(
+            update_leaderboard_multilingual,
+            inputs=[country_selector, metric_checkboxes],
+            outputs=leaderboard_table
+        )
+        
+        metric_checkboxes.change(
+            update_leaderboard_multilingual,
+            inputs=[country_selector, metric_checkboxes],
+            outputs=leaderboard_table
+        )
+        
+        # Update binary table when any input changes
+        target_language_selector.change(
+            update_leaderboard_one_vs_all,
+            inputs=[target_language_selector, languages_checkboxes],
+            outputs=[binary_leaderboard_table, languages_checkboxes]
+        )
+        
+        languages_checkboxes.change(
+            update_leaderboard_one_vs_all,
+            inputs=[target_language_selector, languages_checkboxes],
+            outputs=[binary_leaderboard_table, languages_checkboxes]
+        )
+        
+        # Define load event to run at startup
+        app.load(
+            update_leaderboard_one_vs_all,
+            inputs=[target_language_selector, languages_checkboxes],
+            outputs=[binary_leaderboard_table, languages_checkboxes]
+        )
+        
+        app.load(
+            update_leaderboard_multilingual,
+            inputs=[country_selector, metric_checkboxes],
+            outputs=leaderboard_table
+        )
+        
+    app.launch(allowed_paths=[base_path])