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import requests |
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import json |
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import time |
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import numpy as np |
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from datetime import datetime |
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def log(m): print(f"[{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] {m}", flush=True) |
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def benchmark_fixed_api(): |
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"""Benchmark the fixed high density API""" |
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base_url = "http://localhost:8001" |
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test_scenes = [ |
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"a car near a building", |
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"a person walking a dog in a park", |
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"a beautiful sunset over majestic mountains", |
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"a tree beside a house with flowers", |
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"a bird flying over water near mountains", |
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"a city street with cars and buildings", |
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"a peaceful lake surrounded by trees", |
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"a modern building with glass windows", |
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"a mountain landscape with trees and water", |
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"a person sitting on a bench in a garden" |
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] |
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log("🚀 BENCHMARKING FIXED HIGH DENSITY API...") |
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results = [] |
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for scene in test_scenes: |
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try: |
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start_time = time.time() |
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response = requests.post( |
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f"{base_url}/describe/scene", |
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json={ |
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"scene_description": scene, |
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"enhance_adjectives": True, |
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"include_spatial": True, |
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"adjective_density": 1.0 |
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}, |
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timeout=10 |
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) |
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processing_time = time.time() - start_time |
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if response.status_code == 200: |
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result = response.json() |
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metrics = result["metrics"] |
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results.append({ |
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"input": scene, |
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"adjective_density": metrics["adjective_density"], |
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"adjective_count": metrics["adjective_count"], |
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"spatial_relations": metrics["spatial_relations"], |
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"processing_time": processing_time, |
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"output": result["enhanced_description"] |
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}) |
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log(f"✅ {scene[:40]}... -> Density: {metrics['adjective_density']:.2f}") |
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else: |
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log(f"❌ Failed: {scene}") |
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except Exception as e: |
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log(f"❌ Error: {e}") |
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if results: |
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avg_density = np.mean([r["adjective_density"] for r in results]) |
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avg_adjectives = np.mean([r["adjective_count"] for r in results]) |
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avg_spatial = np.mean([r["spatial_relations"] for r in results]) |
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avg_time = np.mean([r["processing_time"] for r in results]) |
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print(f"\n📊 FIXED HIGH DENSITY BENCHMARK RESULTS:") |
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print(f" • Average Adjective Density: {avg_density:.3f}") |
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print(f" • Average Adjectives/Scene: {avg_adjectives:.1f}") |
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print(f" • Average Spatial Relations: {avg_spatial:.1f}") |
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print(f" • Average Processing Time: {avg_time*1000:.1f}ms") |
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print(f" • Improvement vs Original: {((avg_density - 0.21) / 0.21 * 100):+.1f}%") |
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print(f"\n🎯 SAMPLE OUTPUTS:") |
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for i, result in enumerate(results[:3]): |
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print(f" {i+1}. Input: {result['input']}") |
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print(f" Output: {result['output']}") |
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print(f" Density: {result['adjective_density']:.2f}") |
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return { |
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"avg_adjective_density": avg_density, |
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"avg_adjectives_per_scene": avg_adjectives, |
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"improvement_percent": ((avg_density - 0.21) / 0.21 * 100) |
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} |
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return None |
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if __name__ == "__main__": |
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benchmark_fixed_api() |
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