# Example usage
'''from scripts.run_assessment_prediction_trainer import CompanyModelPipeline
company_ids = ['testid']
input_base_path = '/root/ds_erp_ai/data/raw/erp_assessment_prediction'  # The base path where the raw data for each company is stored

pipeline = CompanyModelPipeline(company_ids=company_ids, input_base_path=input_base_path)
pipeline.run_pipeline()'''

'''from src.pipeline.inference import AssessmentInference

inference = AssessmentInference(
    company_id="testid",num_assessments=2
)

result = inference.run()


print(result)
'''
'''
response2 = bot.predict_next_n_assessment(
    company_info=company_info,
    companyid="testid",
    N=3
)

print(f"Predictions {response2}")


from src.services.chatbot import Chatbot
company_info = {
    'company_name': "ABC Corp",
    'company_size': "Medium",  # Can be "Small", "Medium", or "Large"
    'departments': ["Sales", "Marketing", "IT", "Finance", "HR", "Logistics"]
}
bot = Chatbot()
response = bot.predict_based_on_past_assessment(
    query="Should i make my next assessment weekly or biweekly to meet up to deadline?",
    company_info=company_info,
    companyid="testid"
)

print(f"Result: {response}")'''


from src.services.sop_document_parser import DocumentParser
from src.utils.document_loader import load_document

path = r"/root/ds_erp_ai/data/raw/test_sop.pdf"

parser = DocumentParser()

workers_department = [
    {"name": "sales", "workers": ["sales manager"]}, 
    {"name": "development", "workers": ["deployment officer"]}
]
res = parser.extract_sops_for_workers_by_department(
    docs=load_document(path),  # Load the document for processing
    depts_workers=workers_department
)

print(res)