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app.py

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+import pandas as pd
+import gradio as gr
+import matplotlib.pyplot as plt
+
+# Load CSV once at startup
+CSV_PATH = "collated_results.csv"  # Place your CSV file here
+
+df = pd.read_csv(CSV_PATH).reset_index(drop=True)
+
+# Prepare dropdown choices\ nmodel_choices = sorted(df['model_cls'].dropna().unique().tolist())
+metric_choices = ["num_params_B", "flops_G", "time_plain_s", "mem_plain_GB", "time_compile_s", "mem_compile_GB"]
+group_choices = ["scenario"]
+
+# Analysis function using global df
+def analyze(analysis_type, n_rows, metric, selected_model):
+    columns = df.columns
+    preview_cols = [c for c in columns if c != "model_cls"]
+    
+    if analysis_type == "Preview Data":
+        return df[df['model_cls'] == selected_model][preview_cols].head(n_rows), None
+
+    # ——— Updated Plot Metric ———
+    if analysis_type == "Plot Metric":
+        plot_df = df[df['model_cls'] == selected_model].dropna(subset=[metric])
+        # empty-data guard
+        fig, ax = plt.subplots(figsize=(10, 6))
+        if plot_df.empty:
+            ax.text(0.5, 0.5, 'No data for selected model', ha='center', va='center', fontsize=14)
+            # prettify
+            for spine in ['top','right']:
+                ax.spines[spine].set_visible(False)
+            ax.set_axis_off()
+            return None, fig
+
+        # prepare bars
+        scenarios = plot_df['scenario']
+        values    = plot_df[metric]
+        bars = ax.barh(scenarios, values)
+
+        # prettify
+        fig.set_tight_layout(True)
+        ax.set_xlabel(metric, fontsize=14)
+        ax.set_ylabel('Scenario', fontsize=14)
+        ax.set_title(f"{metric} per Scenario for {selected_model}", fontsize=16)
+        ax.tick_params(axis='both', labelsize=12)
+        ax.grid(axis='x', linestyle='--', alpha=0.5)
+        for spine in ['top','right']:
+            ax.spines[spine].set_visible(False)
+
+        # data labels
+        for bar in bars:
+            w = bar.get_width()
+            ax.text(w + w*0.01, bar.get_y() + bar.get_height()/2,
+                    f"{w:.3f}", va='center', fontsize=12)
+
+        return None, fig
+
+    # ——— Plot Times per Scenario unchanged (already prettified) ———
+    if analysis_type == "Plot Times per Scenario":
+        filt = df[df['model_cls'] == selected_model]
+        filt = filt.dropna(subset=['time_plain_s', 'time_compile_s'])
+        fig, ax = plt.subplots(figsize=(10, 6))
+        if filt.empty:
+            ax.text(0.5, 0.5, 'No data for selected model', ha='center', va='center', fontsize=14)
+            for spine in ['top','right']:
+                ax.spines[spine].set_visible(False)
+            ax.set_axis_off()
+            return None, fig
+
+        scenarios = filt['scenario']
+        plain     = filt['time_plain_s']
+        compile   = filt['time_compile_s']
+        x = range(len(scenarios))
+        width = 0.35
+
+        bars_plain   = ax.bar([i - width/2 for i in x], plain,   width=width, label='Plain')
+        bars_compile = ax.bar([i + width/2 for i in x], compile, width=width, label='Compile')
+
+        ax.set_xticks(x)
+        ax.set_xticklabels(scenarios, rotation=45, ha='right')
+        ax.set_xlabel('Scenario', fontsize=14)
+        ax.set_ylabel('Time (s)', fontsize=14)
+        ax.set_title(f"Plain vs Compile Time for {selected_model}", fontsize=16)
+        ax.tick_params(axis='both', labelsize=12)
+        ax.legend(frameon=False)
+        ax.grid(axis='y', linestyle='--', alpha=0.5)
+        for spine in ['top','right']:
+            ax.spines[spine].set_visible(False)
+
+        # data labels
+        for bar in bars_plain + bars_compile:
+            h = bar.get_height()
+            ax.text(bar.get_x() + bar.get_width()/2, h + h*0.01,
+                    f"{h:.3f}", ha='center', va='bottom', fontsize=12)
+
+        return None, fig
+
+    return None, None
+
+# Build Gradio interface
+demo = gr.Blocks()
+with demo:
+    gr.Markdown("# CSV Data Analyzer")
+
+    model_dropdown = gr.Dropdown(label="Select model_cls", choices=model_choices, value=model_choices[0])
+
+    analysis_type = gr.Radio(
+        choices=["Preview Data", "Plot Metric", "Plot Times per Scenario"],
+        label="Analysis Type",
+        value="Preview Data"
+    )
+
+    n_rows = gr.Slider(5, len(df), step=5, label="Number of rows to preview", value=10)
+    metric = gr.Dropdown(choices=metric_choices, label="Metric to plot", value="time_plain_s")
+
+    analyze_button = gr.Button("Analyze")
+    tbl_output = gr.Dataframe(headers=None, label="Table Output")
+    plot_output = gr.Plot(label="Plot Output")
+
+    analyze_button.click(
+        fn=analyze,
+        inputs=[analysis_type, n_rows, metric, model_dropdown],
+        outputs=[tbl_output, plot_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()

collated_results.csv

@@ -0,0 +1,14 @@
+scenario,model_cls,num_params_B,flops_G,time_plain_s,mem_plain_GB,time_compile_s,mem_compile_GB,fullgraph,mode,github_sha
+black-forest-labs/FLUX.1-dev-bf16,FluxTransformer2DModel,11.9,59529.52,0.534,22.61,0.383,22.7,True,default,807f5113860869a15c6639377745dabce5d0d1ba
+black-forest-labs/FLUX.1-dev-bnb-nf4,FluxTransformer2DModel,5.95,,0.563,6.7,,,,,807f5113860869a15c6639377745dabce5d0d1ba
+black-forest-labs/FLUX.1-dev-layerwise-upcasting,FluxTransformer2DModel,11.9,59529.52,0.599,22.18,,,,,807f5113860869a15c6639377745dabce5d0d1ba
+black-forest-labs/FLUX.1-dev-group-offload-leaf,FluxTransformer2DModel,11.9,59529.52,1.897,0.53,,,,,807f5113860869a15c6639377745dabce5d0d1ba
+Lightricks/LTX-Video-0.9.7-dev-bf16,LTXVideoTransformer3DModel,13.04,167583.45,1.598,25.21,1.079,25.31,True,default,807f5113860869a15c6639377745dabce5d0d1ba
+Lightricks/LTX-Video-0.9.7-dev-layerwise-upcasting,LTXVideoTransformer3DModel,13.04,167583.45,1.656,24.38,,,,,807f5113860869a15c6639377745dabce5d0d1ba
+Lightricks/LTX-Video-0.9.7-dev-group-offload-leaf,LTXVideoTransformer3DModel,13.04,167583.45,2.762,1.04,,,,,807f5113860869a15c6639377745dabce5d0d1ba
+stabilityai/stable-diffusion-xl-base-1.0-bf16,UNet2DConditionModel,2.57,5979.1,0.076,5.05,0.054,5.24,True,default,807f5113860869a15c6639377745dabce5d0d1ba
+stabilityai/stable-diffusion-xl-base-1.0-layerwise-upcasting,UNet2DConditionModel,2.57,5979.1,0.154,4.89,,,,,807f5113860869a15c6639377745dabce5d0d1ba
+stabilityai/stable-diffusion-xl-base-1.0-group-offload-leaf,UNet2DConditionModel,2.57,5979.1,0.524,0.2,,,,,807f5113860869a15c6639377745dabce5d0d1ba
+Wan-AI/Wan2.1-T2V-14B-Diffusers-bf16,WanTransformer3DModel,14.29,785611.67,10.922,31.17,8.456,31.77,True,default,807f5113860869a15c6639377745dabce5d0d1ba
+Wan-AI/Wan2.1-T2V-14B-Diffusers-layerwise-upcasting,WanTransformer3DModel,14.29,785611.67,10.766,26.78,,,,,807f5113860869a15c6639377745dabce5d0d1ba
+Wan-AI/Wan2.1-T2V-14B-Diffusers-group-offload-leaf,WanTransformer3DModel,14.29,785611.67,11.262,4.48,,,,,807f5113860869a15c6639377745dabce5d0d1ba