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2026-02-05 10:15:09 +03:00
parent 2427fce842
commit 67241a5ed0
33 changed files with 13147 additions and 154 deletions
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scheduler_bots/database_fixed.py Normal file
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self.conn.commit()
return True
def process_csv_with_teacher_mapping(self, csv_file):
"""Process CSV with teacher-subject mapping based on positional order"""
if not os.path.exists(csv_file):
return False
with open(csv_file, 'r', encoding='utf-8') as file:
reader = csv.reader(file)
rows = list(reader)
# Identify header row - look for the row containing "ФИО" (full name) or similar indicators
header_idx = None
for i, row in enumerate(rows):
for cell in row:
if "ФИО" in str(cell) or "фио" in str(cell).lower() or "Ф.И.О." in str(cell) or "ф.и.о." in str(cell):
header_idx = i
break
if header_idx is not None:
break
if header_idx is None:
# Check if this file contains class and name columns that identify it as a student data file
# Even if the header doesn't contain ФИО, we might still be able to identify student data
has_class_indicators = any(
any(indicator in str(cell).lower() for cell in row for indicator in ['класс', 'class'])
for row in rows[:min(len(rows), 10)] # Check first 10 rows
)
has_name_indicators = any(
any(indicator in str(cell).lower() for cell in row for indicator in ['имя', 'name', 'фамилия', 'surname'])
for row in rows[:min(len(rows), 10)] # Check first 10 rows
)
if has_class_indicators and has_name_indicators:
# Try to find the header row by looking for class and name indicators
for i, row in enumerate(rows):
if any(indicator in str(cell).lower() for cell in row for indicator in ['класс', 'class']) and \
any(indicator in str(cell).lower() for cell in row for indicator in ['имя', 'name', 'фамилия', 'surname']):
header_idx = i
break
if header_idx is None:
print(f"Skipping {csv_file} - does not appear to be student data with ФИО/class columns")
return False
# Find teacher-subject mappings in the first 0-15 rows before the header
teacher_subject_map = {}
# Build a mapping of subject names in the header row
header_row = rows[header_idx]
header_subjects = {}
for col_idx, subject_name in enumerate(header_row):
subject_name = str(subject_name).strip()
if (subject_name and
subject_name.lower() not in ['ф.и.о.', 'фио', 'класс', 'номер', 'сортировка', 'шкафчика', 'локера'] and
subject_name.strip() != "" and
"ф.и.о" not in subject_name.lower() and
"сортировка" not in subject_name.lower() and
"номер" not in subject_name.lower() and
"№" not in subject_name):
header_subjects[col_idx] = subject_name # Map column index to subject name
# Process rows before the header to find teacher names and map them to subjects
for i in range(min(15, header_idx)): # Check first 15 rows before header
current_row = rows[i]
# Process all cells in the row to find teacher names and their adjacent context
for j, cell_value in enumerate(current_row):
cell_str = str(cell_value).strip()
# Check if this cell is a likely teacher name
if self._is_likely_teacher_name(cell_str):
# Look for context on the left (department) and right (subject)
left_context = ""
right_context = ""
# Get left neighbor (department)
if j > 0 and j-1 < len(current_row):
left_context = str(current_row[j-1]).strip()
# Get right neighbor (subject)
if j < len(current_row) - 1:
right_context = str(current_row[j+1]).strip()
# Try to determine the subject based on adjacency
matched_subject = None
# First priority: right neighbor if it matches a subject in the header
if right_context and j+1 in header_subjects:
matched_subject = header_subjects[j+1]
# Second priority: use left context if it semantically relates to a teacher
elif left_context and any(keyword in left_context.lower() for keyword in ['учитель', 'teacher', 'кафедра', 'department']):
# If left context indicates a department, look for subject to the right of teacher
if j+1 in header_subjects:
matched_subject = header_subjects[j+1]
# If no subject to the right, try to map by position
elif j in header_subjects:
matched_subject = header_subjects[j]
# Third priority: try to map by position
elif j in header_subjects:
matched_subject = header_subjects[j]
# Only add if we don't have a better teacher name for this subject yet
if matched_subject and (matched_subject not in teacher_subject_map or
'Default Teacher for' in teacher_subject_map.get(matched_subject, '')):
teacher_subject_map[matched_subject] = cell_str
# If the cell contains multiple names (separated by newlines), process each separately
elif '\n' in cell_str or '\\n' in cell_str:
cell_parts = [part.strip() for part in cell_str.replace('\\n', '\n').split('\n') if part.strip()]
for part in cell_parts:
if self._is_likely_teacher_name(part):
# Look for context on the left (department) and right (subject)
left_context = ""
right_context = ""
# Get left neighbor (department)
if j > 0 and j-1 < len(current_row):
left_context = str(current_row[j-1]).strip()
# Get right neighbor (subject)
if j < len(current_row) - 1:
right_context = str(current_row[j+1]).strip()
# Try to determine the subject based on adjacency
matched_subject = None
# First priority: right neighbor if it matches a subject in the header
if right_context and j+1 in header_subjects:
matched_subject = header_subjects[j+1]
# Second priority: use left context if it semantically relates to a teacher
elif left_context and any(keyword in left_context.lower() for keyword in ['учитель', 'teacher', 'кафедра', 'department']):
# If left context indicates a department, look for subject to the right of teacher
if j+1 in header_subjects:
matched_subject = header_subjects[j+1]
# If no subject to the right, try to map by position
elif j in header_subjects:
matched_subject = header_subjects[j]
# Third priority: try to map by position
elif j in header_subjects:
matched_subject = header_subjects[j]
# Only add if we don't have a better teacher name for this subject yet
if matched_subject and (matched_subject not in teacher_subject_map or
'Default Teacher for' in teacher_subject_map.get(matched_subject, '')):
teacher_subject_map[matched_subject] = part
# Additional validation: Remove any teacher-subject mappings that seem incorrect
validated_teacher_subject_map = {}
for subject, teacher in teacher_subject_map.items():
# Only add to validated map if teacher name passes all checks
if self._is_likely_teacher_name(teacher):
validated_teacher_subject_map[subject] = teacher
else:
print(f"Warning: Invalid teacher name '{teacher}' detected for subject '{subject}', skipping...")
teacher_subject_map = validated_teacher_subject_map
# Additional teacher-subject mapping: scan the data rows for teacher names paired with subjects
# In many CSV files, teacher names appear in the same rows as subject data
for i in range(header_idx + 1, min(len(rows), header_idx + 50)): # Check first 50 data rows
current_row = rows[i]
for j, cell_value in enumerate(current_row):
cell_str = str(cell_value).strip()
# If cell contains a likely teacher name and corresponds to a subject column
if self._is_likely_teacher_name(cell_str) and j in header_subjects:
subject_name = header_subjects[j]
# Only add if we don't have a better teacher name for this subject yet
if (subject_name not in teacher_subject_map or
'Default Teacher for' in teacher_subject_map.get(subject_name, '')):
teacher_subject_map[subject_name] = cell_str
# Process each student row
for student_row in rows[header_idx + 1:]:
# Determine the structure dynamically based on the header
class_col_idx = None
name_col_idx = None
# Find the index of the class column (usually called "Класс")
for idx, header in enumerate(header_row):
if "Класс" in str(header) or "класс" in str(header) or "Class" in str(header) or "class" in str(header):
class_col_idx = idx
break
# Find the index of the name column (usually called "ФИО")
for idx, header in enumerate(header_row):
if "ФИО" in str(header) or "ф.и.о." in str(header).lower() or "name" in str(header).lower():
name_col_idx = idx
break
# If we couldn't find the columns properly, skip this row
if class_col_idx is None or name_col_idx is None:
continue
# Check if this row has valid data in the expected columns
if (len(student_row) > max(class_col_idx, name_col_idx) and
student_row[class_col_idx].strip() and # class name exists
student_row[name_col_idx].strip() and # student name exists
self._is_valid_student_record_by_cols(student_row, class_col_idx, name_col_idx)):
name = student_row[name_col_idx].strip() # Name column
class_name = student_row[class_col_idx].strip() # Class column
# Insert student into the database
self.cursor.execute(
"INSERT OR IGNORE INTO students (class_name, full_name) VALUES (?, ?)",
(class_name, name)
)
# Get the student_id for this student
self.cursor.execute("SELECT student_id FROM students WHERE full_name = ? AND class_name = ?", (name, class_name))
student_id_result = self.cursor.fetchone()
if student_id_result is None:
continue
student_id = student_id_result[0]
# Process schedule data for this student