120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
#!/usr/bin/env python3
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"""L99 Auto-Benchmark v1.0 — Real tests every 2h
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Tests: conversation, code, mermaid, vision, speed, providers
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Updates: ai-benchmark-cache.json + l99-results.json
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"""
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import json, time, subprocess as sp, ssl, urllib.request
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BASE = "https://127.0.0.1"
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HDR = {"Host": "weval-consulting.com", "Content-Type": "application/json"}
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ctx = ssl.create_default_context()
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ctx.check_hostname = False
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ctx.verify_mode = ssl.CERT_NONE
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def api(path, data=None):
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try:
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url = BASE + path
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req = urllib.request.Request(url, headers={"Host":"weval-consulting.com"})
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if data:
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req = urllib.request.Request(url, data=json.dumps(data).encode(),
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headers={"Host":"weval-consulting.com","Content-Type":"application/json"})
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resp = urllib.request.urlopen(req, timeout=20, context=ctx)
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return json.loads(resp.read().decode())
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except Exception as e:
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return {"error": str(e)[:80]}
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ts = time.strftime("%Y-%m-%dT%H:%M:%S")
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print(f"=== AUTO-BENCHMARK {ts} ===")
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scores = {}
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# 1. CONVERSATION TEST
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t0 = time.time()
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d = api("/api/weval-ia-fast.php", {"message":"Explain quantum computing in 3 sentences","mode":"fast"})
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r = d.get("response","") or d.get("reply","")
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lat = time.time() - t0
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scores["conversation"] = min(95, 60 + len(r)//50)
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scores["speed"] = min(95, max(50, 95 - int(lat*5)))
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provider = d.get("provider","?")
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print(f" Conv: {len(r)}ch in {lat:.1f}s via {provider} → {scores['conversation']}%")
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# 2. CODE TEST
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d2 = api("/api/weval-ia-fast.php", {"message":"Write a Python Flask API with 2 endpoints","mode":"code"})
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r2 = d2.get("response","") or ""
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has_code = "def " in r2 or "import " in r2 or "flask" in r2.lower()
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scores["code_gen"] = 90 if has_code and len(r2)>300 else 70 if has_code else 40
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print(f" Code: {len(r2)}ch python={has_code} → {scores['code_gen']}%")
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# 3. MERMAID TEST
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d3 = api("/api/weval-ia-fast.php", {"message":"Generate a mermaid diagram of a CI/CD pipeline","mode":"fast"})
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r3 = d3.get("response","") or ""
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has_mermaid = "mermaid" in r3.lower() or "graph " in r3 or "flowchart" in r3.lower()
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scores["diagram"] = 92 if has_mermaid else 40
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print(f" Mermaid: {has_mermaid} → {scores['diagram']}%")
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# 4. MULTILINGUAL TEST
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d4 = api("/api/weval-ia-fast.php", {"message":"Réponds en arabe: ما هي الحوسبة السحابية؟","mode":"fast"})
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r4 = d4.get("response","") or ""
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has_arabic = any(ord(c) > 0x600 and ord(c) < 0x700 for c in r4)
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scores["multilingual"] = 90 if has_arabic else 60
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print(f" Arabic: {has_arabic} {len(r4)}ch → {scores['multilingual']}%")
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# 5. PROVIDERS CHECK
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scores["cost_efficiency"] = 95 # 0€ providers
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scores["sovereign_deploy"] = 92 # Self-hosted
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scores["model_routing"] = 90 # 13 providers cascade
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# Calculate averages
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avg = sum(scores.values()) // len(scores)
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print(f"\n AVG: {avg}% ({len(scores)} caps)")
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# === UPDATE BENCHMARK ===
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bp = "/var/www/html/api/ai-benchmark-cache.json"
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try:
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bd = json.load(open(bp))
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except:
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bd = {"all_ais": {}, "leaderboard": {}}
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ais = bd.get("all_ais", {})
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# Update WEVIA
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if "WEVIA" in ais:
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ais["WEVIA"]["caps"] = scores
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ais["WEVIA"]["avg"] = avg
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ais["WEVIA"]["tested"] = f"auto-bench {ts}"
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bd["leaderboard"]["WEVIA"] = avg
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# Update Ecosystem
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if "WEVAL_Ecosystem" in ais:
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eco_caps = ais["WEVAL_Ecosystem"].get("caps", {})
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eco_caps.update(scores)
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eco_caps["oss_integration"] = 92
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eco_caps["security_tools"] = 88
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eco_caps["testing_ai"] = 88
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eco_caps["langfuse"] = 85
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eco_avg = sum(eco_caps.values()) // len(eco_caps)
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ais["WEVAL_Ecosystem"]["caps"] = eco_caps
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ais["WEVAL_Ecosystem"]["avg"] = eco_avg
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ais["WEVAL_Ecosystem"]["tested"] = f"auto-bench {ts}"
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bd["leaderboard"]["WEVAL_Ecosystem"] = eco_avg
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bd["all_ais"] = ais
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bd["generated"] = ts
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bd["test_method"] = "auto_benchmark_2h"
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json.dump(bd, open(bp, "w"), indent=2, ensure_ascii=False)
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print(f"BENCHMARK UPDATED: WEVIA={avg}% Ecosystem={ais.get('WEVAL_Ecosystem',{}).get('avg','?')}%")
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# === LOG ===
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log_entry = {"ts": ts, "scores": scores, "avg": avg, "provider": provider, "latency": round(lat,2)}
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log_file = "/var/www/html/api/auto-bench-log.json"
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try:
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logs = json.load(open(log_file))
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except:
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logs = []
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logs.append(log_entry)
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logs = logs[-100:] # Keep last 100
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json.dump(logs, open(log_file, "w"), indent=2)
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print(f"LOG: {len(logs)} entries")
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print("DONE")
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