updated sops apis and questions generator

test.py

@@ -1,81 +1,3 @@
-# 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)'''
-
-from src.services.chatbot import Chatbot
-
-bot = Chatbot(
-)
-
-company_info =  {
-        "company_name": "Example Co",
-        "company_size": "Medium",
-        "departments": ["HR", "Finance", "IT"]
-    }
-
-response = bot.predict_goal_achievement_probability(
-    company_info=company_info,
-    companyid="testid"
-)
-
-
-print(response)
+import os
+API_KEY = "erp_" + os.urandom(16).hex() 
+print(API_KEY)