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File size: 2,633 Bytes
74626f2 | 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 | import os
import json
import numpy as np
def load_nlp_resources():
nlp_json_path = os.path.join(os.getcwd(), 'backend', 'nlp', 'nlp_resources.json')
print(f"Loading from: {nlp_json_path}")
try:
if not os.path.exists(nlp_json_path):
print(f"File not found: {nlp_json_path}")
return []
with open(nlp_json_path, 'r', encoding='utf-8') as f:
data = json.load(f)
print(f"Data count: {len(data)}")
# Group by difficulty for tiered journey
intro_resources = [r for r in data if int(r.get('difficulty', 2)) <= 3]
medium_resources = [r for r in data if 4 <= int(r.get('difficulty', 2)) <= 7]
advanced_resources = [r for r in data if int(r.get('difficulty', 2)) >= 8]
print(f"Intro: {len(intro_resources)}, Medium: {len(medium_resources)}, Advanced: {len(advanced_resources)}")
# Limit introductory count as requested (top 6 by reward)
intro_resources.sort(key=lambda x: int(x.get('reward', 0)), reverse=True)
intro_resources = intro_resources[:6]
journey_data = intro_resources + medium_resources + advanced_resources
print(f"Journey data count: {len(journey_data)}")
resources = []
for idx, row in enumerate(journey_data):
title = str(row.get('name', f'Resource {idx + 1}')).strip()
module = str(row.get('module', title)).strip()
difficulty = int(row.get('difficulty', 2))
if difficulty <= 3:
y_min, y_max = 16, 19
elif difficulty <= 7:
y_min, y_max = 8, 15
else:
y_min, y_max = 1, 7
x = int(row.get('x', np.random.randint(2, 18)))
y = int(row.get('y', np.random.randint(y_min, y_max + 1)))
resource = {
'id': str(row.get('id', idx + 1)),
'title': title,
'module': module,
'type': str(row.get('type', 'video')),
'difficulty': difficulty,
'reward': int(row.get('reward', 10 * difficulty)),
'position': {'x': x, 'y': y},
'visited': row.get('visited', False)
}
resources.append(resource)
return resources
except Exception as e:
print(f"Error: {e}")
import traceback
traceback.print_exc()
return []
if __name__ == "__main__":
res = load_nlp_resources()
print(f"Final Count: {len(res)}")
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