Create app.py

app.py

@@ -0,0 +1,191 @@
+# app.py
+import os
+import gradio as gr
+import pandas as pd
+
+from ecoeval.config import EcoEvalConfig
+from ecoeval.datasets import load_dataset_by_name, list_available_datasets
+from ecoeval.core import run_benchmark
+from ecoeval.energy import run_with_energy
+from ecoeval.logging_utils import append_run_to_csv, load_leaderboard
+
+
+RUNS_CSV = "runs.csv"
+
+
+def run_ecoeval(model_id: str, dataset_name: str, max_tasks: int):
+    dataset = load_dataset_by_name(dataset_name)
+    if max_tasks is not None and max_tasks > 0:
+        max_tasks = min(max_tasks, len(dataset))
+    else:
+        max_tasks = len(dataset)
+
+    cfg = EcoEvalConfig(
+        model_id=model_id,
+        max_new_tokens=128,
+        temperature=0.2,
+        top_p=0.95,
+    )
+
+    def bench_fn():
+        return run_benchmark(dataset, cfg, limit=max_tasks)
+
+    metrics = run_with_energy(bench_fn, project_name="EcoEval-LLM")
+
+    # Build single-run summary table
+    run_row = {
+        "Model": model_id,
+        "Dataset": dataset_name,
+        "Tasks": metrics["tasks"],
+        "Passed": metrics["passed"],
+        "Accuracy": round(metrics["accuracy"], 3),
+        "Runtime (s)": round(metrics["runtime_seconds"], 2),
+        "Energy (kWh)": (
+            round(metrics["energy_kwh"], 5) if metrics.get("energy_kwh") is not None else None
+        ),
+        "CO2eq (kg)": (
+            round(metrics["emissions_kg"], 5) if metrics.get("emissions_kg") is not None else None
+        ),
+        "Energy / Task (kWh)": (
+            round(metrics["energy_kwh"] / metrics["tasks"], 6)
+            if metrics.get("energy_kwh") is not None and metrics["tasks"] > 0
+            else None
+        ),
+        "CO2eq / Passed (kg)": (
+            round(metrics["emissions_kg"] / metrics["passed"], 6)
+            if metrics.get("emissions_kg") is not None and metrics["passed"] > 0
+            else None
+        ),
+    }
+
+    summary_df = pd.DataFrame([run_row])
+
+    # Persist run to leaderboard CSV
+    append_run_to_csv(RUNS_CSV, run_row)
+
+    summary_text = (
+        f"### Run summary\n"
+        f"- **Model**: `{model_id}`\n"
+        f"- **Dataset**: `{dataset_name}`\n"
+        f"- **Tasks**: {metrics['tasks']}\n"
+        f"- **Passed**: {metrics['passed']}  \n"
+        f"- **Accuracy**: {metrics['accuracy']:.3f}\n"
+        f"- **Runtime**: {metrics['runtime_seconds']:.2f} s\n"
+        f"- **Energy**: {metrics.get('energy_kwh', 'N/A')} kWh\n"
+        f"- **CO₂eq**: {metrics.get('emissions_kg', 'N/A')} kg\n"
+    )
+
+    per_task_df = pd.DataFrame(metrics["per_task"])
+
+    return summary_df, summary_text, per_task_df
+
+
+def refresh_leaderboard():
+    df = load_leaderboard(RUNS_CSV)
+    if df is None or df.empty:
+        return pd.DataFrame()
+    # Sort by accuracy descending, then energy ascending
+    sort_cols = []
+    if "Accuracy" in df.columns:
+        sort_cols.append("Accuracy")
+    if "Energy (kWh)" in df.columns:
+        sort_cols.append("Energy (kWh)")
+    if sort_cols:
+        df = df.sort_values(by=["Accuracy", "Energy (kWh)"], ascending=[False, True])
+    return df.reset_index(drop=True)
+
+
+def build_app():
+    dataset_options = list_available_datasets()
+
+    with gr.Blocks(title="EcoEval-LLM: Energy & Carbon Benchmarking for LLM Code Generation") as demo:
+        gr.Markdown(
+            """
+# 🌱 EcoEval-LLM
+Evaluate code generation models on **correctness**, **runtime**, **energy usage**, and **carbon emissions**.
+
+This Space runs a small code-generation benchmark, executes unit tests, and tracks energy & CO₂ with [CodeCarbon](https://github.com/mlco2/codecarbon).
+"""
+        )
+
+        with gr.Tab("Run Benchmark"):
+            with gr.Row():
+                model_in = gr.Textbox(
+                    label="Model ID (Hugging Face Hub)",
+                    value="Salesforce/codegen-350M-multi",
+                    info="Any causal LM checkpoint that can generate Python code.",
+                )
+                dataset_in = gr.Dropdown(
+                    choices=dataset_options,
+                    value=dataset_options[0],
+                    label="Dataset",
+                )
+
+            max_tasks_in = gr.Slider(
+                minimum=1,
+                maximum=50,
+                step=1,
+                value=5,
+                label="Max tasks to evaluate",
+                info="For heavy models, start small.",
+            )
+
+            run_btn = gr.Button("🚀 Run EcoEval Benchmark", variant="primary")
+
+            gr.Markdown("### Run-level metrics")
+            summary_table = gr.Dataframe(
+                headers=[
+                    "Model",
+                    "Dataset",
+                    "Tasks",
+                    "Passed",
+                    "Accuracy",
+                    "Runtime (s)",
+                    "Energy (kWh)",
+                    "CO2eq (kg)",
+                    "Energy / Task (kWh)",
+                    "CO2eq / Passed (kg)",
+                ],
+                interactive=False,
+                wrap=True,
+            )
+
+            summary_md = gr.Markdown()
+
+            gr.Markdown("### Per-task results")
+            per_task_table = gr.Dataframe(
+                headers=[
+                    "task_id",
+                    "prompt_preview",
+                    "passed",
+                    "runtime_s",
+                ],
+                interactive=False,
+                wrap=True,
+            )
+
+            run_btn.click(
+                fn=run_ecoeval,
+                inputs=[model_in, dataset_in, max_tasks_in],
+                outputs=[summary_table, summary_md, per_task_table],
+            )
+
+        with gr.Tab("Leaderboard"):
+            gr.Markdown(
+                "Global history of runs in this Space (sorted by accuracy, then energy)."
+            )
+            refresh_btn = gr.Button("🔄 Refresh leaderboard")
+            leaderboard_table = gr.Dataframe(interactive=False, wrap=True)
+
+            refresh_btn.click(
+                fn=refresh_leaderboard,
+                inputs=None,
+                outputs=leaderboard_table,
+            )
+
+    return demo
+
+
+if __name__ == "__main__":
+    demo = build_app()
+    demo.launch()