48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
# Example usage
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'''from scripts.run_assessment_prediction_trainer import CompanyModelPipeline
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company_ids = ['testid']
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input_base_path = '/root/ds_erp_ai/data/raw/erp_assessment_prediction' # The base path where the raw data for each company is stored
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pipeline = CompanyModelPipeline(company_ids=company_ids, input_base_path=input_base_path)
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pipeline.run_pipeline()'''
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'''from src.pipeline.inference import AssessmentInference
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inference = AssessmentInference(
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company_id="testid",num_assessments=2
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)
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result = inference.run()
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print(result)
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'''
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'''
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response2 = bot.predict_next_n_assessment(
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company_info=company_info,
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companyid="testid",
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N=3
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)
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print(f"Predictions {response2}")
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'''
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from src.services.chatbot import Chatbot
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company_info = {
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'company_name': "ABC Corp",
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'company_size': "Medium", # Can be "Small", "Medium", or "Large"
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'departments': ["Sales", "Marketing", "IT", "Finance", "HR", "Logistics"]
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}
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bot = Chatbot()
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response = bot.predict_based_on_past_assessment(
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query="Should i make my next assessment weekly or biweekly to meet up to deadline?",
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company_info=company_info,
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companyid="testid"
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)
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print(f"Result: {response}")
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