Create app.py

Benchmarking of llm models

app.py

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+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Hugging Face Space: LLM Benchmarking App using Gradio
+- Upload config.yaml and dataset.jsonl
+- Select task
+- Run benchmarking across multiple models
+- Compute metrics: Exact Match, F1, ROUGE-L, BLEU
+- Display results and allow CSV download
+"""
+
+import os
+import time
+import json
+import yaml
+import gradio as gr
+import pandas as pd
+from tqdm import tqdm
+
+# Optional metrics
+try:
+    from rouge_score import rouge_scorer
+except ImportError:
+    rouge_scorer = None
+
+try:
+    import sacrebleu
+except ImportError:
+    sacrebleu = None
+
+# ---------------- Metrics ---------------- #
+def exact_match(pred, ref):
+    return float(pred.strip().lower() == ref.strip().lower())
+
+def token_f1(pred, ref):
+    pred_tokens = pred.lower().split()
+    ref_tokens = ref.lower().split()
+    if not pred_tokens and not ref_tokens:
+        return 1.0
+    if not pred_tokens or not ref_tokens:
+        return 0.0
+    common = sum(min(pred_tokens.count(t), ref_tokens.count(t)) for t in set(pred_tokens))
+    precision = common / len(pred_tokens)
+    recall = common / len(ref_tokens)
+    return 2 * precision * recall / (precision + recall) if precision + recall else 0.0
+
+def rouge_l(pred, ref):
+    if rouge_scorer:
+        scorer = rouge_scorer.RougeScorer(["rougeL"], use_stemmer=True)
+        return scorer.score(ref, pred)["rougeL"].fmeasure
+    return 0.0
+
+def bleu(pred, ref):
+    if sacrebleu:
+        return sacrebleu.corpus_bleu([pred], [[ref]]).score
+    return 0.0
+
+def compute_metrics(task, prediction, reference):
+    metrics = {}
+    if task in ("qa", "classification"):
+        metrics["exact_match"] = exact_match(prediction, reference)
+        metrics["f1"] = token_f1(prediction, reference)
+    elif task in ("summarization", "translation"):
+        metrics["rougeL_f"] = rouge_l(prediction, reference)
+        metrics["bleu"] = bleu(prediction, reference)
+    else:
+        metrics["f1"] = token_f1(prediction, reference)
+    return metrics
+
+# ---------------- Hugging Face Inference ---------------- #
+def hf_generate(model_name, prompt, max_new_tokens=256, temperature=0.2):
+    from huggingface_hub import InferenceClient
+    client = InferenceClient(model=model_name, token=os.getenv("HUGGINGFACE_HUB_TOKEN"))
+    start = time.time()
+    try:
+        output = client.text_generation(prompt, max_new_tokens=max_new_tokens, temperature=temperature)
+        latency = time.time() - start
+        return output.strip(), latency
+    except Exception as e:
+        return f"ERROR: {e}", time.time() - start
+
+# ---------------- Benchmark Function ---------------- #
+def benchmark(config_text, dataset_text, task):
+    cfg = yaml.safe_load(config_text)
+    data = [json.loads(line) for line in dataset_text.splitlines() if line.strip()]
+
+    models = cfg.get("models", [])
+    templates = cfg.get("prompt_templates", {})
+    template = templates.get(task, "{{text}}")
+
+    results = []
+    for m in 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 tqdm(data, desc=model_name):
+            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
+            }
+            results.append(row)
+
+    df = pd.DataFrame(results)
+    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"]:
+            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)
+
+# ---------------- 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)")
+    run_btn = gr.Button("Run Benchmark")
+
+    results_table = gr.Dataframe(headers=["model","id","task","prediction","reference","latency_seconds","exact_match","f1","rougeL_f","bleu"], 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):
+        config_text = open(config_path, "r", encoding="utf-8").read()
+        dataset_text = open(dataset_path, "r", encoding="utf-8").read()
+        df, summary = benchmark(config_text, dataset_text, task)
+        csv_path = "results.csv"
+        df.to_csv(csv_path, index=False)
+        return df, summary, csv_path
+
+    run_btn.click(run_benchmark, inputs=[config_file, dataset_file, task], outputs=[results_table, summary_box, download_csv])
+
+demo.launch()