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63bcd5a 5e95de5 63bcd5a 5e95de5 63bcd5a 5e95de5 63bcd5a 5e95de5 63bcd5a 5e95de5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | import logging
import pandas as pd
from src.similarity_model import find_similar_projects
from src.similarity_model import load_metadata
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s | %(levelname)s | %(message)s"
)
logger = logging.getLogger(__name__)
TOP_K = 5
SELF_TEST_SAMPLES = 20
def run_self_test():
df = load_metadata()
total = min(len(df), SELF_TEST_SAMPLES)
success = 0
for i in range(total):
row = df.loc[i]
results = find_similar_projects(
title=row.get("project_title", ""),
abstract=row.get("abstract", ""),
description=row.get("description", ""),
features=row.get("features", []),
top_k=1
)
if "project_id" in results.columns:
pred = int(results.iloc[0]["project_id"])
if pred == i:
success += 1
score = success / total
print("\n==============================")
print("SELF RETRIEVAL TEST")
print("==============================")
print(f"Projects Tested : {total}")
print(f"Top1 Accuracy : {score:.2%}")
print("==============================")
return score
def run_real_queries():
queries = [
{
"title":
"AI Clinic Management System",
"description":
"""
Smart clinic with booking,
chatbot, patient records,
doctor dashboard.
"""
},
{
"title":
"Smart Library Assistant",
"description":
"""
Library app with chatbot,
recommendation system,
qr code borrowing.
"""
},
{
"title":
"Attendance Face Recognition",
"description":
"""
Attendance system using
face recognition and reports.
"""
},
{
"title":
"E-commerce Recommendation Platform",
"description":
"""
Online shopping website with
recommendation engine,
payments and dashboard.
"""
}
]
print("\n==============================")
print("REAL QUERY TEST")
print("==============================")
total_score = 0
count = 0
for q in queries:
results = find_similar_projects(
title=q["title"],
description=q["description"],
top_k=1
)
if "hybrid_score" in results.columns:
score = float(
results.iloc[0]["hybrid_score"]
)
risk = str(
results.iloc[0]["duplicate_risk"]
)
top_title = str(
results.iloc[0]["project_title"]
)
total_score += score
count += 1
print()
print("Query:", q["title"])
print("Top Match:", top_title)
print("Score:", round(score, 4))
print("Risk:", risk)
avg = total_score / count if count else 0
print("\n==============================")
print(f"Average Query Score: {avg:.4f}")
print("==============================")
return avg
def final_status(
self_score,
query_score
):
print("\n==============================")
print("FINAL MODEL STATUS")
print("==============================")
final_score = (
0.60 * self_score +
0.40 * query_score
)
if final_score >= 0.90:
print("EXCELLENT [OK]")
elif final_score >= 0.75:
print("VERY GOOD [OK]")
elif final_score >= 0.60:
print("GOOD [WARN]")
else:
print("NEEDS IMPROVEMENT [FAIL]")
print("Overall Score:", round(final_score, 4))
print("==============================\n")
if __name__ == "__main__":
self_score = run_self_test()
query_score = run_real_queries()
final_status(self_score, query_score)
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