Create app.py

app.py

@@ -0,0 +1,50 @@
+import os
+import gradio as gr
+from datasets import load_dataset
+
+auth_token = os.environ.get("auth_token")
+visit_bench = load_dataset("mlfoundations/visit-bench", use_auth_token=auth_token)['test']
+
+df = visit_bench.to_pandas()
+print(f"Got {len(df)} items in dataframe")
+df = df.sample(frac=1)
+
+LINES_NUMBER = 20
+
+def display_df():
+    df_images = df.head(LINES_NUMBER)
+    return df_images
+
+def display_next(dataframe, end):
+    start = int(end or len(dataframe))
+    end = int(start) + int(LINES_NUMBER)
+    global df
+    if end >= len(df) - 1:
+        start = 0
+        end = LINES_NUMBER
+        df = df.sample(frac=1)
+        print(f"Shuffle")
+    df_images = df.iloc[start:end]
+    assert len(df_images) == LINES_NUMBER
+    return df_images, end
+
+initial_dataframe = display_df()
+
+# Gradio Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("<h1><center>VisIT-Bench Dataset Viewer</center></h1>")
+
+    with gr.Row():
+        num_end = gr.Number(visible=False)
+        b1 = gr.Button("Get Initial dataframe")
+        b2 = gr.Button("Next Rows")
+
+    with gr.Row():
+        out_dataframe = gr.Dataframe(initial_dataframe, wrap=True, max_rows=LINES_NUMBER, overflow_row_behaviour="paginate",
+                                     interactive=False)
+
+    b1.click(fn=display_df, outputs=out_dataframe, api_name="initial_dataframe")
+    b2.click(fn=display_next, inputs=[out_dataframe, num_end], outputs=[out_dataframe, num_end],
+             api_name="next_rows")
+
+demo.launch(debug=True, show_error=True)