162 lines
6.7 KiB
Python
162 lines
6.7 KiB
Python
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"""
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Quick test to verify the complete integration workflow.
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Run this after starting both services to test end-to-end.
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"""
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import requests
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import json
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import time
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def test_complete_workflow():
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"""Test the complete workflow from raw telemetry to strategy generation."""
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print("🧪 Testing Complete Integration Workflow\n")
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print("=" * 70)
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# Test 1: Check services are running
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print("\n1️⃣ Checking service health...")
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try:
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enrichment_health = requests.get("http://localhost:8000/healthz", timeout=2)
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print(f" ✅ Enrichment service: {enrichment_health.json()}")
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except Exception as e:
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print(f" ❌ Enrichment service not responding: {e}")
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print(" → Start with: uvicorn hpcsim.api:app --port 8000")
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return False
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try:
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ai_health = requests.get("http://localhost:9000/api/health", timeout=2)
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print(f" ✅ AI Intelligence Layer: {ai_health.json()}")
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except Exception as e:
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print(f" ❌ AI Intelligence Layer not responding: {e}")
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print(" → Start with: cd ai_intelligence_layer && uvicorn main:app --port 9000")
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return False
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# Test 2: Send telemetry with race context
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print("\n2️⃣ Sending telemetry with race context...")
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telemetry_samples = []
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for lap in range(1, 6):
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sample = {
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'lap_number': lap,
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'total_laps': 51,
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'speed': 280.0 + (lap * 2),
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'throttle': 0.85 + (lap * 0.01),
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'brake': 0.05,
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'tire_compound': 'MEDIUM',
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'tire_life_laps': lap,
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'track_temperature': 42.5,
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'rainfall': False,
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'track_name': 'Monza',
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'driver_name': 'Alonso',
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'current_position': 5,
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'fuel_level': 0.9 - (lap * 0.02),
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}
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telemetry_samples.append(sample)
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responses = []
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for i, sample in enumerate(telemetry_samples, 1):
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try:
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response = requests.post(
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"http://localhost:8000/ingest/telemetry",
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json=sample,
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timeout=5
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)
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if response.status_code == 200:
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result = response.json()
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responses.append(result)
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print(f" Lap {sample['lap_number']}: ✅ Enriched")
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# Check if we got enriched_telemetry and race_context
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if 'enriched_telemetry' in result and 'race_context' in result:
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print(f" └─ Enriched telemetry + race context included")
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if i == len(telemetry_samples):
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# Show last response details
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enriched = result['enriched_telemetry']
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context = result['race_context']
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print(f"\n 📊 Final Enriched Metrics:")
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print(f" - Aero Efficiency: {enriched['aero_efficiency']:.3f}")
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print(f" - Tire Degradation: {enriched['tire_degradation_index']:.3f}")
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print(f" - Driver Consistency: {enriched['driver_consistency']:.3f}")
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print(f"\n 🏎️ Race Context:")
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print(f" - Track: {context['race_context']['race_info']['track_name']}")
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print(f" - Lap: {context['race_context']['race_info']['current_lap']}/{context['race_context']['race_info']['total_laps']}")
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print(f" - Position: P{context['race_context']['driver_state']['current_position']}")
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print(f" - Fuel: {context['race_context']['driver_state']['fuel_remaining_percent']:.1f}%")
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print(f" - Competitors: {len(context['race_context']['competitors'])} shown")
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else:
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print(f" ⚠️ Legacy format (no race context)")
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else:
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print(f" Lap {sample['lap_number']}: ❌ Failed ({response.status_code})")
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except Exception as e:
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print(f" Lap {sample['lap_number']}: ❌ Error: {e}")
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time.sleep(0.5) # Small delay between requests
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# Test 3: Check AI layer buffer
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print("\n3️⃣ Checking AI layer webhook processing...")
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# The AI layer should have received webhooks and auto-generated strategies
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# Let's verify by checking if we can call brainstorm manually
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# (The auto-brainstorm happens in the webhook, but we can verify the buffer)
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print(" ℹ️ Auto-brainstorming triggers when buffer has ≥3 laps")
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print(" ℹ️ Strategies are returned in the webhook response to enrichment service")
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print(" ℹ️ Check the AI Intelligence Layer logs for auto-generated strategies")
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# Test 4: Manual brainstorm call (to verify the endpoint still works)
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print("\n4️⃣ Testing manual brainstorm endpoint...")
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try:
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brainstorm_request = {
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"race_context": {
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"race_info": {
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"track_name": "Monza",
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"total_laps": 51,
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"current_lap": 5,
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"weather_condition": "Dry",
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"track_temp_celsius": 42.5
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},
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"driver_state": {
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"driver_name": "Alonso",
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"current_position": 5,
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"current_tire_compound": "medium",
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"tire_age_laps": 5,
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"fuel_remaining_percent": 82.0
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},
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"competitors": []
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}
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}
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response = requests.post(
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"http://localhost:9000/api/strategy/brainstorm",
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json=brainstorm_request,
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timeout=30
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)
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if response.status_code == 200:
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result = response.json()
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print(f" ✅ Generated {len(result['strategies'])} strategies")
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if result['strategies']:
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strategy = result['strategies'][0]
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print(f"\n 🎯 Sample Strategy:")
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print(f" - Name: {strategy['strategy_name']}")
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print(f" - Stops: {strategy['stop_count']}")
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print(f" - Pit Laps: {strategy['pit_laps']}")
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print(f" - Tires: {' → '.join(strategy['tire_sequence'])}")
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print(f" - Risk: {strategy['risk_level']}")
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else:
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print(f" ⚠️ Brainstorm returned {response.status_code}")
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print(f" (This might be expected if Gemini API is not configured)")
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except Exception as e:
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print(f" ℹ️ Manual brainstorm skipped: {e}")
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print("\n" + "=" * 70)
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print("✅ Integration test complete!\n")
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return True
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if __name__ == '__main__':
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test_complete_workflow()
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