fix

app.py

@@ -7,9 +7,13 @@ import pandas as pd
 import gradio as gr
 import os
 
-# MODEL_SIZES = pd.read_pickle(
-#     "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/resolve/main/model_size_cache.pkl"
-# )
+
+def restart_space():
+    HfApi().restart_space(
+        repo_id="https://huggingface.co/spaces/IlyasMoutawwakil/llm-bar-race",
+        token=os.environ.get("HF_TOKEN", None),
+    )
+
 
 # read in the data
 open_llm_race_dataset = pd.read_csv(
@@ -39,10 +43,26 @@ open_llm_race_dataset["type"] = open_llm_race_dataset["model"].apply(
     lambda x: MODEL_TYPES[x].name if x in MODEL_TYPES else ModelType.Unknown.name
 )
 
-# # add the model size
-# open_llm_race_dataset["size"] = open_llm_race_dataset["model"].apply(
-#     lambda x: MODEL_SIZES[x] if x in MODEL_SIZES else None
-# )
+
+def get_bar_chart(model_type: str):
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.set_xlim(0, 100)
+    subset = open_llm_race_dataset[open_llm_race_dataset["type"] == model_type]
+    subset = subset.pivot(index="date", columns="model", values="score")
+    subset.fillna(0, inplace=True)
+    fig = bcr.bar_chart_race(
+        subset,
+        n_bars=10,
+        fixed_max=True,
+        period_length=1000,
+        steps_per_period=20,
+        end_period_pause=100,
+        bar_texttemplate="{x:.2f}",
+        filter_column_colors=True,
+        fig=fig,
+    )
+    gr.HTML(fig.data)
+
 
 # Demo interface
 demo = gr.Blocks()
@@ -52,98 +72,12 @@ with demo:
 
     with gr.Tabs():
         with gr.TabItem(label="Pretrained Models"):
-            pretrained_fig, ax = plt.subplots(figsize=(12, 6))
-            ax.set_xlim(0, 100)
-            pretrained_dataset = open_llm_race_dataset[
-                open_llm_race_dataset["type"] == ModelType.PT.name
-            ]
-            pretrained_dataset = pretrained_dataset.pivot(
-                index="date", columns="model", values="score"
-            )
-            pretrained_dataset.fillna(0, inplace=True)
-            pretrained_fig = bcr.bar_chart_race(
-                pretrained_dataset,
-                n_bars=10,
-                fixed_max=True,
-                period_length=1000,
-                steps_per_period=20,
-                end_period_pause=100,
-                bar_texttemplate="{x:.2f}",
-                filter_column_colors=True,
-                fig=pretrained_fig,
-            )
-            gr.HTML(pretrained_fig.data)
-
+            get_bar_chart(ModelType.PT.name)
         with gr.TabItem(label="Instructions Finetuend Models"):
-            inst_finetuned_fig, ax = plt.subplots(figsize=(12, 6))
-            ax.set_xlim(0, 100)
-            inst_finetuned_dataset = open_llm_race_dataset[
-                open_llm_race_dataset["type"] == ModelType.IFT.name
-            ]
-            inst_finetuned_dataset = inst_finetuned_dataset.pivot(
-                index="date", columns="model", values="score"
-            )
-            inst_finetuned_dataset.fillna(0, inplace=True)
-            inst_finetuned_fig = bcr.bar_chart_race(
-                inst_finetuned_dataset,
-                n_bars=10,
-                fixed_max=True,
-                period_length=1000,
-                steps_per_period=20,
-                end_period_pause=100,
-                bar_texttemplate="{x:.2f}",
-                filter_column_colors=True,
-                fig=inst_finetuned_fig,
-            )
-            gr.HTML(inst_finetuned_fig.data)
+            get_bar_chart(ModelType.IFT.name)
 
         with gr.TabItem(label="RLHF Models"):
-            rl_fig, ax = plt.subplots(figsize=(12, 6))
-            ax.set_xlim(0, 100)
-            rl_dataset = open_llm_race_dataset[
-                open_llm_race_dataset["type"] == ModelType.IFT.name
-            ]
-            rl_dataset = rl_dataset.pivot(index="date", columns="model", values="score")
-            rl_dataset.fillna(0, inplace=True)
-            rl_fig = bcr.bar_chart_race(
-                rl_dataset,
-                n_bars=10,
-                fixed_max=True,
-                period_length=1000,
-                steps_per_period=20,
-                end_period_pause=100,
-                bar_texttemplate="{x:.2f}",
-                filter_column_colors=True,
-                fig=rl_fig,
-            )
-            gr.HTML(rl_fig.data)
-
-        # with gr.TabItem(label="Finetuned Models"):
-        #     finetuned_dataset = open_llm_race_dataset[
-        #         open_llm_race_dataset["type"] == ModelType.FT.name
-        #     ]
-        #     finetuned_dataset = finetuned_dataset.pivot(
-        #         index="date", columns="model", values="score"
-        #     )
-        # finetuned_fig = bcr.bar_chart_race(
-        #     finetuned_dataset,
-        #     n_bars=10,
-        #     fixed_max=True,
-        #     period_length=1000,
-        #     steps_per_period=20,
-        #     end_period_pause=100,
-        #     bar_texttemplate="{x:.2f}",
-        #     filter_column_colors=True,
-        #     fig=pretrained_fig,
-        # )
-        # gr.HTML(finetuned_fig.data)
-
-
-def restart_space():
-    HfApi().restart_space(
-        repo_id="https://huggingface.co/spaces/IlyasMoutawwakil/llm-bar-race",
-        token=os.environ.get("HF_TOKEN", None),
-    )
+            get_bar_chart(ModelType.RL.name)
 
 
 # Restart space every hour
@@ -154,6 +88,4 @@ scheduler.add_job(
     seconds=3600,
 )
 scheduler.start()
-
-
 demo.queue(concurrency_count=10).launch()