import neo4j, json

# dbprep
fsroot = '/autograder/source/'
datasets = ['Neo4J_dataset']
db = neo4j.GraphDatabase.driver('bolt://localhost:7687', auth = ('neo4j', '4Sfz541Lm')).session()
def evaluate(query : str):
    query = query.strip()
    return db.run(query).data() if query else None

while True:
    try:
        db.run('RETURN 0')
        break
    except:
        continue

for d in datasets:    
    with open(fsroot + d + '.txt', encoding = 'utf-8') as f:
        db.run(f.read())

from solution import sols
answers = [evaluate(s) if type(s) is str else tuple(evaluate(k) for k in s) for s in sols ]

# grading
from os import listdir
from importlib.util import module_from_spec, spec_from_file_location
subroot = '/autograder/submission/'
feedback = ''
submissions = [subroot + f for f in listdir(subroot) if f.strip().lower().endswith('.py')]

grade = 0
n_queries = len(sols)

if submissions: 
    submission = submissions[0]
    
    for i in range(n_queries):
        feedback += f'Query {i + 1}: '
        try:
            spec = spec_from_file_location('curr', submission)
            module = module_from_spec(spec)
            spec.loader.exec_module(module)
            q = getattr(module, f'query{i + 1}')()
            def eq(a: list, b):
                if a is None: return False
                if type(b) is tuple:
                    return any(eq(a, bb) for bb in b)
                if len(a) != len(b): return False
                return all(aa == bb for aa, bb in zip(a, b))
                
            if eq(evaluate(q), answers[i]):
                grade += 1
                feedback += 'Correct.\n'
            else:
                feedback += 'Wrong Answer.\n'
        except Exception:
            feedback += 'Runtime Error.\n'
else:
    feedback += 'No python file in submission.\n'    

# output
results = {
    'output': feedback,
    'score': round(grade * 100 / n_queries, 1), 
    'max_score': 100,
}

with open('/autograder/results/results.json', 'w') as res:
    json.dump(results, res)