data incorproration

app.py

@@ -2,13 +2,17 @@ import pandas as pd
 import gradio as gr
 import random
 
-def generate_random_data():
-    """Generate random scatter plot data."""
-    n_points = random.randint(20, 50)
-    return pd.DataFrame({
-        "x": [random.uniform(-100, 100) for _ in range(n_points)],
-        "y": [random.uniform(-100, 100) for _ in range(n_points)]
-    })
+from data import ModelBenchmarkData
+
+
+DATA = ModelBenchmarkData("data.json")
+
+
+def refresh_plot_data():
+    data = DATA.get_ttft_tpot_data(estimator="median", use_cuda_time=False)
+    print(data)
+    return pd.DataFrame(data)
+
 
 def load_css():
     """Load CSS styling."""
@@ -20,7 +24,7 @@ def load_css():
 
 def refresh_plot():
     """Generate new random data and update description."""
-    return generate_random_data(), "**Transformer CI Dashboard**<br>-<br>**AMD runs on MI325**<br>**NVIDIA runs on A10**<br><br>*This dashboard only tracks important models*<br>*(Data refreshed)*"
+    return refresh_plot_data(), "**Transformer CI Dashboard**<br>-<br>**AMD runs on MI325**<br>**NVIDIA runs on A10**<br><br>*This dashboard only tracks important models*<br>*(Data refreshed)*"
 
 # Create Gradio interface
 with gr.Blocks(title="Random Data Dashboard", css=load_css(), fill_height=True, fill_width=True) as demo:
@@ -34,8 +38,9 @@ with gr.Blocks(title="Random Data Dashboard", css=load_css(), fill_height=True,
         # Main plot area
         with gr.Column(elem_classes=["main-content"]):
             plot = gr.ScatterPlot(
-                generate_random_data(),
-                x="x", y="y",
+                refresh_plot_data(),
+                x="ttft", y="tpot",
+                tooltip="all",
                 height="100vh",
                 container=False,
                 show_fullscreen_button=True,
@@ -46,4 +51,4 @@ with gr.Blocks(title="Random Data Dashboard", css=load_css(), fill_height=True,
     summary_btn.click(fn=refresh_plot, outputs=[plot, description])
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

data.py

@@ -16,15 +16,13 @@ class ModelBenchmarkData:
         with open(json_path, "r") as f:
             self.data = json.load(f)
 
-    def get_ttft_tpot_data(self, model_name: str, estimator: str = "median", use_cuda_time: bool = False) -> dict:
-        data_points = []
+    def get_ttft_tpot_data(self, estimator: str = "median", use_cuda_time: bool = False) -> dict:
+        aggregated_data = {"ttft": [], "tpot": [], "label": []}
         time_key = "cuda_time" if use_cuda_time else "wall_time"
         for cfg_name, data in self.data.items():
             x_measures = [d[time_key] for d in data["ttft"]]
             y_measures = [d[time_key] for d in data["tpot"]]
-            data_points.append({
-                "x": estimate_from_measures(x_measures, estimator),
-                "y": estimate_from_measures(y_measures, estimator),
-                "label": cfg_name,
-            })
-        return data_points
+            aggregated_data["ttft"].append(estimate_from_measures(x_measures, estimator))
+            aggregated_data["tpot"].append(estimate_from_measures(y_measures, estimator))
+            aggregated_data["label"].append(cfg_name)
+        return aggregated_data