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#!/usr/bin/env python3
"""
Basic Performance Test for Curriculum Assistant
Tests core concepts without external dependencies
"""
import time
import os
from pathlib import Path
import json
class BasicCurriculumTest:
def __init__(self):
self.response_cache = {}
self.sample_data = {
"loops": {
"filename": "Week 6 lesson.pptx (1).pdf",
"page": 1,
"content": "Loops are programming constructs that solve the problem of repetition. Instead of writing hundreds of print statements to count from 1 to 100, loops allow you to accomplish the same task with just a few lines of code."
},
"variables": {
"filename": "Week 4 Lesson.pptx (2).pdf",
"page": 2,
"content": "Variables are containers that store data values. They allow you to save and reuse information in your programs."
},
"functions": {
"filename": "Week 5 lesson.pptx.pdf",
"page": 3,
"content": "Functions are reusable blocks of code that perform specific tasks. They help organize code and avoid repetition."
}
}
print("✅ Basic test initialized")
def simple_search(self, query):
"""Simple keyword-based search"""
start_time = time.time()
# Check cache
if query in self.response_cache:
print(f"✅ Cache hit! Response time: {time.time() - start_time:.3f}s")
return self.response_cache[query]
# Simple keyword search
query_lower = query.lower()
results = []
for topic, data in self.sample_data.items():
if query_lower in topic.lower() or query_lower in data['content'].lower():
results.append({
'topic': topic,
'filename': data['filename'],
'page': data['page'],
'content': data['content'],
'score': data['content'].lower().count(query_lower)
})
# Sort by relevance
results.sort(key=lambda x: x['score'], reverse=True)
# Generate response
if results:
best_result = results[0]
response = f"📄 Found in: {best_result['filename']} - Page {best_result['page']}\n\n"
response += f"Topic: {best_result['topic']}\n"
response += f"Content: {best_result['content']}\n\n"
response += f"Found {len(results)} relevant topics"
else:
response = f"No relevant content found for '{query}'"
# Cache result
self.response_cache[query] = response
response_time = time.time() - start_time
print(f"✅ Response generated in {response_time:.3f} seconds")
return response
def test_performance():
"""Run performance tests"""
print("🚀 Starting Basic Performance Test...")
# Initialize
start_time = time.time()
chatbot = BasicCurriculumTest()
init_time = time.time() - start_time
print(f"✅ Initialization time: {init_time:.3f} seconds")
# Test queries
test_queries = [
"loops",
"variables",
"functions",
"programming",
"for loop",
"while loop"
]
print(f"\n🧪 Testing {len(test_queries)} queries...")
total_time = 0
for i, query in enumerate(test_queries, 1):
print(f"\n--- Test {i}/{len(test_queries)}: '{query}' ---")
start_time = time.time()
response = chatbot.simple_search(query)
query_time = time.time() - start_time
total_time += query_time
print(f"Response time: {query_time:.3f}s")
print(f"Response length: {len(response)} characters")
print(f"Cache size: {len(chatbot.response_cache)} entries")
# Show first 150 chars of response
print(f"Response preview: {response[:150]}...")
# Summary
avg_time = total_time / len(test_queries)
print(f"\n📊 Performance Summary:")
print(f"Total time: {total_time:.3f}s")
print(f"Average response time: {avg_time:.3f}s")
print(f"Initialization time: {init_time:.3f}s")
print(f"Cache hits: {len([q for q in test_queries if q in chatbot.response_cache])}")
# Performance rating
if avg_time < 0.001:
rating = "🚀 EXCELLENT (< 1ms)"
elif avg_time < 0.01:
rating = "✅ GOOD (< 10ms)"
elif avg_time < 0.1:
rating = "⚠️ ACCEPTABLE (< 100ms)"
else:
rating = "❌ SLOW (> 100ms)"
print(f"Performance rating: {rating}")
# Comparison with 10-minute response time
if avg_time < 600: # 10 minutes = 600 seconds
improvement = 600 / avg_time if avg_time > 0 else float('inf')
print(f"🚀 This is {improvement:.0f}x faster than the 10-minute response time!")
else:
print("❌ This is still slower than the 10-minute response time")
if __name__ == "__main__":
test_performance() |