Update app.py

app.py

@@ -1,5 +1,5 @@
 import os
-
+from apscheduler.schedulers.background import BackgroundScheduler
 import gradio as gr
 
 from src.control_panel import create_control_panel, create_control_callback
@@ -25,78 +25,83 @@ MACHINE_TO_HARDWARE = {"hf-dgx-01": "A100-80GB-275W 🖥️", "audace": "RTX4090
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 
 
-demo = gr.Blocks(css=custom_css)
-with demo:
-    gr.HTML(LOGO, elem_classes="logo")
-    gr.HTML(TITLE, elem_classes="title")
-    gr.Markdown(INTRODUCTION, elem_classes="descriptive-text")
-    ####################### HARDWARE TABS #######################
-    with gr.Tabs(elem_classes="tabs"):
-        for id, (machine, hardware) in enumerate(MACHINE_TO_HARDWARE.items()):
-            with gr.TabItem(hardware, id=id):
-                ####################### CONTROL PANEL #######################
-                (
-                    filter_button,
-                    machine_textbox,
-                    search_bar,
-                    score_slider,
-                    memory_slider,
-                    backend_checkboxes,
-                    datatype_checkboxes,
-                    optimization_checkboxes,
-                    quantization_checkboxes,
-                ) = create_control_panel()
-                ####################### HARDWARE SUBTABS #######################
-                with gr.Tabs(elem_classes="subtabs"):
-                    llm_perf_df = get_llm_perf_df(machine=machine)
-                    ####################### LEADERBOARD TAB #######################
-                    with gr.TabItem("Leaderboard 🏅", id=0):
-                        leaderboard_table = create_leaderboard_table(llm_perf_df)
-                        lat_score_mem_plot = create_lat_score_mem_plot(llm_perf_df)
-                    ####################### BETTERTRANSFORMER SPEEDUP TAB #######################
-                    with gr.TabItem("BetterTransformer 📈", id=2):
-                        bt_prefill_plot, bt_decode_plot = create_bt_plots(llm_perf_df)
-                    with gr.TabItem("FlashAttentionV2 📈", id=3):
-                        fa2_prefill_plot, fa2_decode_plot = create_fa2_plots(llm_perf_df)
-                    with gr.TabItem("Custom Quantization Kernels 📈", id=4):
-                        quant_prefill_plot, quant_decode_plot = create_quant_plots(llm_perf_df)
-
-                ####################### CONTROL CALLBACK #######################
-                create_control_callback(
-                    filter_button,
-                    # inputs
-                    machine_textbox,
-                    search_bar,
-                    score_slider,
-                    memory_slider,
-                    backend_checkboxes,
-                    datatype_checkboxes,
-                    optimization_checkboxes,
-                    quantization_checkboxes,
-                    # outputs
-                    leaderboard_table,
-                    lat_score_mem_plot,
-                    bt_prefill_plot,
-                    bt_decode_plot,
-                    fa2_prefill_plot,
-                    fa2_decode_plot,
-                    quant_prefill_plot,
-                    quant_decode_plot,
-                )
-        ####################### ABOUT TAB #######################
-        with gr.TabItem("About 📖", id=3):
-            gr.HTML(ABOUT, elem_classes="descriptive-text")
-            gr.Markdown(EXAMPLE_CONFIG, elem_classes="descriptive-text")
-    ####################### CITATION
-    with gr.Row():
-        with gr.Accordion("📙 Citation", open=False):
-            citation_button = gr.Textbox(
-                value=CITATION_BUTTON,
-                label=CITATION_BUTTON_LABEL,
-                elem_id="citation-button",
-                show_copy_button=True,
-            )
-
-if __name__ == "__main__":
-    # Launch demo
-    demo.queue().launch()
+llm_perf_df = get_llm_perf_df(machine="hf-dgx-01")
+leaderboard_table = create_leaderboard_table(llm_perf_df)
+
+import unicodedata
+
+def is_valid_unicode(char):
+    try:
+        unicodedata.name(char)
+        return True  # Valid Unicode character
+    except ValueError:
+        return False  # Invalid Unicode character
+
+def remove_invalid_unicode(input_string):
+    if isinstance(input_string, str):
+        valid_chars = [char for char in input_string if is_valid_unicode(char)]
+        return ''.join(valid_chars)
+    else:
+        return input_string  # Return non-string values as is
+
+def display(x, y):
+    # Assuming df is your DataFrame
+    for column in leaderboard_table.columns:
+        if leaderboard_table[column].dtype == 'object':
+            leaderboard_table[column] = leaderboard_table[column].apply(remove_invalid_unicode)
+
+    subset_df = leaderboard_df[COLS]
+    return subset_df
+
+dummy1 = gr.Textbox(visible=False)
+
+INTRODUCTION_TEXT = """
+This is a copied space from LLM Trustworthy Leaderboard. Instead of displaying
+the results as table this space was modified to simply provides a gradio API interface. 
+Using the following python script below, users can access the full leaderboard data easily.
+Python on how to access the data:
+```python
+# Import dependencies
+from gradio_client import Client
+# Initialize the Gradio client with the API URL
+client = Client("https://rodrigomasini-data-only-llm-perf-leaderboard.hf.space/")
+try:
+    # Perform the API call
+    response = client.predict("","", api_name='/predict')
+    # Check if response it's directly accessible
+    if len(response) > 0:
+        print("Response received!")
+        headers = response.get('headers', [])
+        data = response.get('data', [])
+        print(headers)
+        # Remove commenst if you want to download the dataset and save in csv format
+        # Specify the path to your CSV file
+        #csv_file_path = 'llm-perf-benchmark.csv'
+        # Open the CSV file for writing
+        #with open(csv_file_path, mode='w', newline='', encoding='utf-8') as file:
+        #    writer = csv.writer(file)
+            # Write the headers
+        #    writer.writerow(headers)
+            # Write the data
+        #    for row in data:
+        #        writer.writerow(row)
+        #print(f"Results saved to {csv_file_path}")
+    # If the above line prints a string that looks like JSON, you can parse it with json.loads(response)
+    # Otherwise, you might need to adjust based on the actual structure of `response`
+except Exception as e:
+    print(f"An error occurred: {e}")
+```
+"""
+
+interface = gr.Interface(
+    fn=display,
+    inputs=[gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text"), dummy1],
+    outputs=[hidden_leaderboard_table_for_search]
+)
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=1800)
+
+scheduler.start()
+
+interface.launch()