lint

app.py

@@ -1,73 +1,93 @@
 import gradio as gr
 import pandas as pd
 import json
-from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS
+from constants import (
+    BANNER,
+    INTRODUCTION_TEXT,
+    CITATION_TEXT,
+    METRICS_TAB_TEXT,
+    DIR_OUTPUT_REQUESTS,
+)
 from init import is_model_on_hub, upload_file, load_all_info_from_dataset_hub
-from utils_display import AutoEvalColumn, fields, make_clickable_model, styled_error, styled_message
+from utils_display import (
+    AutoEvalColumn,
+    fields,
+    make_clickable_model,
+    styled_error,
+    styled_message,
+)
 from datetime import datetime, timezone
 
 LAST_UPDATED = "September, 7th 2023"
 GPU_MODEL = "NVIDIA Tesla M60"
 
-column_names = {"model": "model",
-                "AP-IoU=0.50:0.95-area=all-maxDets=100": "AP",
-                "AP-IoU=0.50-area=all-maxDets=100": "AP@.50",
-                "AP-IoU=0.75-area=all-maxDets=100": "AP@.75",
-                "AP-IoU=0.50:0.95-area=small-maxDets=100" : "AP-S",
-                "AP-IoU=0.50:0.95-area=medium-maxDets=100": "AP-M",
-                "AP-IoU=0.50:0.95-area=large-maxDets=100": "AP-L",
-                "AR-IoU=0.50:0.95-area=all-maxDets=1": "AR1",
-                "AR-IoU=0.50:0.95-area=all-maxDets=10": "AR10",
-                "AR-IoU=0.50:0.95-area=all-maxDets=100": "AR100",
-                "AR-IoU=0.50:0.95-area=small-maxDets=100": "AR-S",
-                "AR-IoU=0.50:0.95-area=medium-maxDets=100": "AR-M",
-                "AR-IoU=0.50:0.95-area=large-maxDets=100": "AR-L",
-                "estimated_fps": "FPS(*)",
-                "hub_license": "hub license",
-            }
+column_names = {
+    "model": "model",
+    "AP-IoU=0.50:0.95-area=all-maxDets=100": "AP",
+    "AP-IoU=0.50-area=all-maxDets=100": "AP@.50",
+    "AP-IoU=0.75-area=all-maxDets=100": "AP@.75",
+    "AP-IoU=0.50:0.95-area=small-maxDets=100": "AP-S",
+    "AP-IoU=0.50:0.95-area=medium-maxDets=100": "AP-M",
+    "AP-IoU=0.50:0.95-area=large-maxDets=100": "AP-L",
+    "AR-IoU=0.50:0.95-area=all-maxDets=1": "AR1",
+    "AR-IoU=0.50:0.95-area=all-maxDets=10": "AR10",
+    "AR-IoU=0.50:0.95-area=all-maxDets=100": "AR100",
+    "AR-IoU=0.50:0.95-area=small-maxDets=100": "AR-S",
+    "AR-IoU=0.50:0.95-area=medium-maxDets=100": "AR-M",
+    "AR-IoU=0.50:0.95-area=large-maxDets=100": "AR-L",
+    "estimated_fps": "FPS(*)",
+    "hub_license": "hub license",
+}
 
 eval_queue_repo, requested_models, csv_results = load_all_info_from_dataset_hub()
 
 if not csv_results.exists():
     raise Exception(f"CSV file {csv_results} does not exist locally")
-    
+
 # Get csv with data and parse columns
 original_df = pd.read_csv(csv_results)
 lst_evaluated_models = original_df["model"].tolist()
 lst_evaluated_models = list(map(str.lower, lst_evaluated_models))
 
+
 # Formats the columns
 def decimal_formatter(x):
     x = "{:.2f}".format(x)
     return x
 
+
 def perc_formatter(x):
     x = "{:.2%}".format(x)
     while len(x) < 6:
         x = f"0{x}"
     return x
 
+
 # Drop columns not specified in dictionary
 cols_to_drop = [col for col in original_df.columns if col not in column_names]
 original_df.drop(cols_to_drop, axis=1, inplace=True)
 
 for col in original_df.columns:
     if col == "model":
-        original_df[col] = original_df[col].apply(lambda x: x.replace(x, make_clickable_model(x)))
+        original_df[col] = original_df[col].apply(
+            lambda x: x.replace(x, make_clickable_model(x))
+        )
     elif col == "estimated_fps":
-        original_df[col] = original_df[col].apply(decimal_formatter) # For decimal values
+        original_df[col] = original_df[col].apply(
+            decimal_formatter
+        )  # For decimal values
     elif col == "hub_license":
         continue
     else:
-        original_df[col] = original_df[col].apply(perc_formatter) # For % values
- 
+        original_df[col] = original_df[col].apply(perc_formatter)  # For % values
+
 original_df.rename(columns=column_names, inplace=True)
 
 COLS = [c.name for c in fields(AutoEvalColumn)]
 TYPES = [c.type for c in fields(AutoEvalColumn)]
 
+
 def request_model(model_text, chbcoco2017):
-    
     # Determine the selected checkboxes
     dataset_selection = []
     if chbcoco2017:
@@ -75,33 +95,37 @@ def request_model(model_text, chbcoco2017):
 
     if len(dataset_selection) == 0:
         return styled_error("You need to select at least one dataset")
-    
-    # Check if model exists on the hub    
+
+    # Check if model exists on the hub
     base_model_on_hub, error_msg = is_model_on_hub(model_text)
     if not base_model_on_hub:
         return styled_error(f"Base model '{model_text}' {error_msg}")
-    
+
     # Check if model is already evaluated
-    model_text = model_text.replace(" ","")
+    model_text = model_text.replace(" ", "")
     if model_text.lower() in lst_evaluated_models:
-        return styled_error(f"Results of the model '{model_text}' are now ready and available.")
-    
+        return styled_error(
+            f"Results of the model '{model_text}' are now ready and available."
+        )
+
     # Construct the output dictionary
     current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-    required_datasets = ', '.join(dataset_selection)
+    required_datasets = ", ".join(dataset_selection)
     eval_entry = {
         "date": current_time,
         "model": model_text,
-        "datasets_selected": required_datasets
+        "datasets_selected": required_datasets,
     }
-    
-    # Prepare file path 
+
+    # Prepare file path
     DIR_OUTPUT_REQUESTS.mkdir(parents=True, exist_ok=True)
-    
-    fn_datasets = '@ '.join(dataset_selection)
-    filename = model_text.replace("/","@") + "@@" + fn_datasets 
+
+    fn_datasets = "@ ".join(dataset_selection)
+    filename = model_text.replace("/", "@") + "@@" + fn_datasets
     if filename in requested_models:
-        return styled_error(f"A request for this model '{model_text}' and dataset(s) was already made.")
+        return styled_error(
+            f"A request for this model '{model_text}' and dataset(s) was already made."
+        )
     try:
         filename_ext = filename + ".txt"
         out_filepath = DIR_OUTPUT_REQUESTS / filename_ext
@@ -109,18 +133,21 @@ def request_model(model_text, chbcoco2017):
         # Write the results to a text file
         with open(out_filepath, "w") as f:
             f.write(json.dumps(eval_entry))
-            
+
         upload_file(filename, out_filepath)
-        
+
         # Include file in the list of uploaded files
         requested_models.append(filename)
-        
+
         # Remove the local file
         out_filepath.unlink()
 
-        return styled_message("🤗 Your request has been submitted and will be evaluated soon!</p>")
-    except Exception as e:
-        return styled_error(f"Error submitting request!")
+        return styled_message(
+            "🤗 Your request has been submitted and will be evaluated soon!</p>"
+        )
+    except Exception:
+        return styled_error("Error submitting request!")
+
 
 with gr.Blocks() as demo:
     gr.HTML(BANNER, elem_id="banner")
@@ -131,39 +158,57 @@ with gr.Blocks() as demo:
             leaderboard_table = gr.components.Dataframe(
                 value=original_df,
                 datatype=TYPES,
-                max_rows=None,
                 elem_id="leaderboard-table",
                 interactive=False,
                 visible=True,
-                )
+            )
 
         with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1):
             gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text")
 
-        with gr.TabItem("✉️✨ Request a model here!", elem_id="od-benchmark-tab-table", id=2):
+        with gr.TabItem(
+            "✉️✨ Request a model here!", elem_id="od-benchmark-tab-table", id=2
+        ):
             with gr.Column():
-                gr.Markdown("# ✉️✨ Request results for a new model here!", elem_classes="markdown-text")
+                gr.Markdown(
+                    "# ✉️✨ Request results for a new model here!",
+                    elem_classes="markdown-text",
+                )
             with gr.Column():
                 gr.Markdown("Select a dataset:", elem_classes="markdown-text")
                 with gr.Column():
-                    model_name_textbox = gr.Textbox(label="Model name (user_name/model_name)")
-                    chb_coco2017 = gr.Checkbox(label="COCO validation 2017 dataset", visible=False, value=True, interactive=False)
+                    model_name_textbox = gr.Textbox(
+                        label="Model name (user_name/model_name)"
+                    )
+                    chb_coco2017 = gr.Checkbox(
+                        label="COCO validation 2017 dataset",
+                        visible=False,
+                        value=True,
+                        interactive=False,
+                    )
                 with gr.Column():
                     mdw_submission_result = gr.Markdown()
                     btn_submitt = gr.Button(value="🚀 Request")
-                    btn_submitt.click(request_model, 
-                                      [model_name_textbox, chb_coco2017], 
-                                      mdw_submission_result)
-
-    gr.Markdown(f"(*) FPS was measured using *{GPU_MODEL}* processing 1 image per batch. Refer to the 📈 \"Metrics\" tab for further details.", elem_classes="markdown-text")
+                    btn_submitt.click(
+                        request_model,
+                        [model_name_textbox, chb_coco2017],
+                        mdw_submission_result,
+                    )
+
+    gr.Markdown(
+        f'(*) FPS was measured using *{GPU_MODEL}* processing 1 image per batch. Refer to the 📈 "Metrics" tab for further details.',
+        elem_classes="markdown-text",
+    )
     gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text")
 
     with gr.Row():
         with gr.Accordion("📙 Citation", open=False):
             gr.Textbox(
-                value=CITATION_TEXT, lines=7,
+                value=CITATION_TEXT,
+                lines=7,
                 label="Copy the BibTeX snippet to cite this source",
                 elem_id="citation-button",
-            ).style(show_copy_button=True)
+                show_copy_button=True,
+            )
 
 demo.launch()