Cleanup app launching and error handling for FileNotFound

app.py

@@ -140,66 +140,79 @@ def evaluate():
 
 
 def load_dataset(local_dataset, hf_dataset):
-    if local_dataset:
-        EVALUATION["dataset_id"] = os.path.splitext(
-            os.path.basename(local_dataset.name)
-        )[0]
-        EVALUATION["source"] = "Local Dataset"
-        EVALUATION["df"] = pd.read_csv(local_dataset.name)
-    else:
-        EVALUATION["dataset_id"] = hf_dataset
-        EVALUATION["source"] = "HuggingFace Hub"
-        EVALUATION["df"] = hf_load_dataset(hf_dataset, split="train[0:100]").to_pandas()
-
-    columns = EVALUATION["df"].select_dtypes(include=["object"]).columns.tolist()
-    column_corpus = EVALUATION["df"][columns[0]].tolist()[:5]
-
-    dataset_sampling_method = gr.Radio(
-        label="Scope",
-        info="Determines the scope of the dataset to be analyzed",
-        choices=["First", "Last", "Random"],
-        value="First",
-        visible=True,
-        interactive=True,
-    )
+    try:
+        if local_dataset:
+            EVALUATION["dataset_id"] = os.path.splitext(
+                os.path.basename(local_dataset.name)
+            )[0]
+            EVALUATION["source"] = "Local Dataset"
+            EVALUATION["df"] = pd.read_csv(local_dataset.name)
+        else:
+            EVALUATION["dataset_id"] = hf_dataset
+            EVALUATION["source"] = "HuggingFace Hub"
+            EVALUATION["df"] = hf_load_dataset(
+                hf_dataset, split="train[0:100]"
+            ).to_pandas()
+
+        columns = EVALUATION["df"].select_dtypes(include=["object"]).columns.tolist()
+        column_corpus = EVALUATION["df"][columns[0]].tolist()[:5]
+
+        dataset_sampling_method = gr.Radio(
+            label="Scope",
+            info="Determines the scope of the dataset to be analyzed",
+            choices=["First", "Last", "Random"],
+            value="First",
+            visible=True,
+            interactive=True,
+        )
 
-    dataset_sampling_size = gr.Slider(
-        label=f"Number of Entries",
-        info=f"Determines the number of entries to be analyzed. Due to computational constraints, the maximum number of entries that can be analyzed is {SAMPLING_SIZE_THRESHOLD}.",
-        minimum=1,
-        maximum=min(EVALUATION["df"].shape[0], SAMPLING_SIZE_THRESHOLD),
-        value=min(EVALUATION["df"].shape[0], SAMPLING_SIZE_THRESHOLD),
-        visible=True,
-        interactive=True,
-    )
+        dataset_sampling_size = gr.Slider(
+            label=f"Number of Entries",
+            info=f"Determines the number of entries to be analyzed. Due to computational constraints, the maximum number of entries that can be analyzed is {SAMPLING_SIZE_THRESHOLD}.",
+            minimum=1,
+            maximum=min(EVALUATION["df"].shape[0], SAMPLING_SIZE_THRESHOLD),
+            value=min(EVALUATION["df"].shape[0], SAMPLING_SIZE_THRESHOLD),
+            visible=True,
+            interactive=True,
+        )
 
-    dataset_column = gr.Radio(
-        label="Column",
-        info="Determines the column to be analyzed. These are the columns with text data.",
-        choices=columns,
-        value=columns[0],
-        visible=True,
-        interactive=True,
-    )
+        dataset_column = gr.Radio(
+            label="Column",
+            info="Determines the column to be analyzed. These are the columns with text data.",
+            choices=columns,
+            value=columns[0],
+            visible=True,
+            interactive=True,
+        )
 
-    dataset_column_corpus = gr.Dataframe(
-        value=pd.DataFrame({f"{columns[0]}": column_corpus}), visible=True
-    )
+        dataset_column_corpus = gr.Dataframe(
+            value=pd.DataFrame({f"{columns[0]}": column_corpus}), visible=True
+        )
 
-    dataset_import_btn = gr.Button(
-        value="Import Dataset",
-        interactive=True,
-        variant="primary",
-        visible=True,
-    )
+        dataset_import_btn = gr.Button(
+            value="Import Dataset",
+            interactive=True,
+            variant="primary",
+            visible=True,
+        )
 
-    return (
-        dataset_sampling_method,
-        dataset_sampling_size,
-        dataset_column,
-        dataset_column_corpus,
-        dataset_import_btn,
-    )
+        return (
+            dataset_sampling_method,
+            dataset_sampling_size,
+            dataset_column,
+            dataset_column_corpus,
+            dataset_import_btn,
+        )
+
+    except FileNotFoundError as e:
+        print(f"FileNotFoundError: {e}")
+        return (
+            gr.Radio(visible=False),
+            gr.Slider(visible=False),
+            gr.Radio(visible=False),
+            gr.Dataframe(visible=False),
+            gr.Button(visible=False),
+        )
 
 
 def import_dataset(dataset_sampling_method, dataset_sampling_size, dataset_column):
@@ -453,35 +466,3 @@ with BiasAware:
 
 if __name__ == "__main__":
     BiasAware.launch()
-
-
-if __name__ == "__main__":
-    BiasAware.launch()
-
-
-if __name__ == "__main__":
-    BiasAware.launch()
-
-
-if __name__ == "__main__":
-    BiasAware.launch()
-
-
-if __name__ == "__main__":
-    BiasAware.launch()
-
-
-if __name__ == "__main__":
-    BiasAware.launch()
-
-
-if __name__ == "__main__":
-    BiasAware.launch()
-
-
-if __name__ == "__main__":
-    BiasAware.launch()
-
-
-if __name__ == "__main__":
-    BiasAware.launch()