Update app.py

app.py

@@ -1,4 +1,3 @@
-
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 """
@@ -172,101 +171,3 @@ def hf_judge(model_name, prompt, candidate, reference=None, rubric=None, max_new
         return json.loads(m.group(0)) if m else {"raw": text}
     except Exception as e:
         return {"error": str(e)}
-
-
-# ---------------- Gradio UI ---------------- #
-
-# ---------------- Gradio UI ---------------- #
-with gr.Blocks() as demo:
-    gr.Markdown("# LLM Benchmarking App (Hugging Face)")
-    gr.Markdown("Upload config.yaml and dataset.jsonl, select task, and run benchmark.")
-
-    with gr.Row():
-        config_file = gr.File(label="Upload Config (YAML)", type="filepath")
-        dataset_file = gr.File(label="Upload Dataset (JSONL)", type="filepath")
-
-    task = gr.Textbox(label="Task (e.g., qa, summarization, classification)")
-    use_judge = gr.Checkbox(label="Use Judge Model?", value=False)
-    judge_model = gr.Textbox(label="Judge Model (HF repo id)", value="mistralai/Mistral-7B-Instruct")
-    rubric = gr.Textbox(label="Judge Rubric", value="Evaluate relevance, factuality, clarity. Return JSON.")
-
-    run_btn = gr.Button("Run Benchmark")
-
-    results_table = gr.Dataframe(headers=[
-        "model","id","task","prediction","reference","latency_seconds",
-        "exact_match","f1","rougeL_f","bleu","judge_relevance","judge_factuality","judge_clarity","judge_overall"
-    ], label="Results")
-
-    summary_box = gr.Textbox(label="Summary", lines=10)
-    download_csv = gr.File(label="Download CSV")
-
-    def run_benchmark(config_path, dataset_path, task, use_judge=False, judge_model=None, rubric=None):
-        if not config_path or not dataset_path:
-            return None, "Error: Please upload both files", None
-
-        config_text = open(config_path, "r", encoding="utf-8").read()
-        dataset_text = open(dataset_path, "r", encoding="utf-8").read()
-        cfg = yaml.safe_load(config_text)
-        data = [json.loads(line) for line in dataset_text.splitlines() if line.strip()]
-        template = cfg.get("prompt_templates", {}).get(task, "{{text}}")
-
-        results = []
-        for m in cfg.get("models", []):
-            model_name = m["name"]
-            max_tokens = m.get("params", {}).get("max_tokens", 256)
-            temperature = m.get("params", {}).get("temperature", 0.2)
-
-            for ex in data:
-                variables = {k: ex.get(k, "") for k in ("question", "context", "text", "labels")}
-                prompt = template
-                for k, v in variables.items():
-                    prompt = prompt.replace(f"{{{{{k}}}}}", str(v))
-
-                prediction, latency = hf_generate(model_name, prompt, max_new_tokens=max_tokens, temperature=temperature)
-                metrics = compute_metrics(task, prediction, ex.get("reference", ""))
-
-                row = {
-                    "model": model_name,
-                    "id": ex.get("id", ""),
-                    "task": task,
-                    "prompt": prompt,
-                    "prediction": prediction,
-                    "reference": ex.get("reference", ""),
-                    "latency_seconds": latency,
-                    **metrics
-                }
-
-                if use_judge and judge_model:
-                    scores = hf_judge(judge_model, prompt, prediction, ex.get("reference", ""), rubric)
-                    for k, v in (scores.items() if isinstance(scores, dict) else []):
-                        row[f"judge_{k}"] = v
-
-                results.append(row)
-
-        df = pd.DataFrame(results)
-        csv_path = "results.csv"
-        df.to_csv(csv_path, index=False)
-
-        # Summary
-        summary = []
-        for model_name in set(df["model"]):
-            sub = df[df["model"] == model_name]
-            summary.append(f"## {model_name}")
-            summary.append(f"Samples: {len(sub)}")
-            for metric in ["exact_match", "f1", "rougeL_f", "bleu", "judge_overall"]:
-                if metric in sub.columns:
-                    vals = [v for v in sub[metric] if isinstance(v, (int, float))]
-                    if vals:
-                        summary.append(f"{metric}: mean={sum(vals)/len(vals):.4f}")
-            summary.append(f"Latency mean: {sum(sub['latency_seconds'])/len(sub):.3f}s\n")
-
-        return df, "\n".join(summary), csv_path
-
-    run_btn.click(
-        run_benchmark,
-        inputs=[config_file, dataset_file, task, use_judge, judge_model, rubric],
-        outputs=[results_table, summary_box, download_csv]
-    )
-
-demo.launch()
-