# -*- encoding: utf-8 -*- """ Copyright (c) App-Generator.dev | AppSeed.us """ import random, string, json, statistics, re, pprint, time from datetime import datetime from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT from django.conf import settings from django.http import JsonResponse from .common import * from .h_util import * from .h_code_parser import * from .h_django import * def model_suggest_charts(aModelClassImport, aDebug=False): start_time = time.time() retVal = COMMON.ERR model_class = name_to_class( aModelClassImport ) if not model_class: print( f" > ERR getting class for model [{aModelClassImport}]" ) return retVal, None, None, None retVal, csv_content = h_model_to_csv( aModelClassImport ) if COMMON.OK != retVal: print( f" > ERR getting CSV Representation for model [{aModelClassImport}]" ) return retVal, None, None, None csv_header = csv_content[0] csv_content.pop() aQuestion = f"I need to extract charts from a CSV with the following fields: {csv_header}." aQuestion += "Here are the first lines from the file." idx = 0 for l in csv_content: idx += 1 if idx > 5: break aQuestion += l + '\n' aQuestion += '\nBased on the above information and field data, please suggest all relevant charts and context information in JSON format with folowing sections:' aQuestion += '\nNode Summary: with a title about the data provided in the CSV and a description with full input.' aQuestion += '\nAnoter node "potential_uses" where are suggestions regarding the reports we can get from the input.' aQuestion += '\nSuggested charts node will present a list of chart types with a short explanation and the relevant fields correlated with the x-axis, y-axis.' aQuestion += '\nHere is the expected response that should be a valid JSON without extra text fields in response (helps response extraction and processing):' aQuestion += '\n{' aQuestion += '\n "summary":{' aQuestion += '\n "title":"CONTENT_HERE",' aQuestion += '\n "description":"CONTENT_HERE",' aQuestion += '\n "},' aQuestion += '\n "potential_uses":[' aQuestion += '\n "FIRST use explanation",' aQuestion += '\n "And the next ones",' aQuestion += '\n "],' aQuestion += '\n "suggested_charts":[' aQuestion += '\n {},' aQuestion += '\n {},' aQuestion += '\n "],' aQuestion += '\n}' if aDebug: print('>>>>>>>>>>>>>>>>>>>>>>>>') print( aQuestion ) print('<<<<<<<<<<<<<<<<<<<<<<<<') message = f"{HUMAN_PROMPT}{aQuestion}\n\n{AI_PROMPT}" client = Anthropic(api_key=getattr(settings, 'ANTHROPIC_API_KEY')) response = None response_title = None response_json = None response_conclusion = None response_data = None try: response = client.completions.create( model="claude-2.1", prompt=message, max_tokens_to_sample=1000, ) response = response.completion.split('```') response_title = response[0] response_json = response[1].replace('json', '') response_conclusion = response[2] response_data = json.loads(response_json) retVal = COMMON.OK except json.JSONDecodeError: print(f"> ERR: {str(e)}") retVal = COMMON.ERR except Exception as e: print(f"> ERR: {str(e)}") retVal = COMMON.ERR print("--- %s seconds ---" % (time.time() - start_time)) return retVal, response_title, response_conclusion, response_data ''' aCvsFile needs to be in `media` folder ''' def csv_suggest_charts(aCvsFile, aDebug=False): start_time = time.time() retVal = COMMON.ERR csv_content = file_load( os.path.join('media', aCvsFile), True ) if not csv_content: print( f" > Input file [{aCvsFile}], not found in the MEDIA folder" ) return retVal, None, None, None csv_header = csv_content[0] csv_content.pop() aQuestion = f"I need to extract charts from a CSV with the following fields: {csv_header}." aQuestion += "Here are the first lines from the file." idx = 0 for l in csv_content: idx += 1 if idx > 5: break aQuestion += l + '\n' aQuestion += '\nBased on the above information and field data, please suggest all relevant charts and context information in JSON format with folowing sections:' aQuestion += '\nNode Summary: with a title about the data provided in the CSV and a description with full input.' aQuestion += '\nAnoter node "potential_uses" where are suggestions regarding the reports we can get from the input.' aQuestion += '\nSuggested charts node will present a list of chart types with a short explanation and the relevant fields correlated with the x-axis, y-axis.' aQuestion += '\nHere is the expected response that should be a valid JSON without extra text fields in response (helps response extraction and processing):' aQuestion += '\n{' aQuestion += '\n "summary":{' aQuestion += '\n "title":"CONTENT_HERE",' aQuestion += '\n "description":"CONTENT_HERE",' aQuestion += '\n "},' aQuestion += '\n "potential_uses":[' aQuestion += '\n "FIRST use explanation",' aQuestion += '\n "And the next ones",' aQuestion += '\n "],' aQuestion += '\n "suggested_charts":[' aQuestion += '\n {},' aQuestion += '\n {},' aQuestion += '\n "],' aQuestion += '\n}' if aDebug: print('>>>>>>>>>>>>>>>>>>>>>>>>') print( aQuestion ) print('<<<<<<<<<<<<<<<<<<<<<<<<') message = f"{HUMAN_PROMPT}{aQuestion}\n\n{AI_PROMPT}" client = Anthropic(api_key=getattr(settings, 'ANTHROPIC_API_KEY')) response = None response_title = None response_json = None response_conclusion = None response_data = None try: response = client.completions.create( model="claude-2.1", prompt=message, max_tokens_to_sample=1000, ) response = response.completion.split('```') response_title = response[0] response_json = response[1].replace('json', '') response_conclusion = response[2] response_data = json.loads(response_json) retVal = COMMON.OK except json.JSONDecodeError: print(f"> ERR: {str(e)}") retVal = COMMON.ERR except Exception as e: print(f"> ERR: {str(e)}") retVal = COMMON.ERR print("--- %s seconds ---" % (time.time() - start_time)) return retVal, response_title, response_conclusion, response_data ''' aCvsFile needs to be in `media` folder ''' def csv_query(aCvsFile, aDataQuery, aRowLimit=10, aDebug=False): start_time = time.time() retVal = COMMON.ERR csv_content = file_load( os.path.join('media', aCvsFile), True ) if not csv_content: print( f" > Input file [{aCvsFile}], not found in the MEDIA folder" ) return retVal, None, None, None csv_header = csv_content[0] csv_content.pop() aQuestion = f"{aDataQuery,} from this CSV file with the following fields: {csv_header}." aQuestion += "Here is the content." idx = 0 for l in csv_content: idx += 1 if idx > aRowLimit: break aQuestion += l + '\n' aQuestion += '\n}' if aDebug: print('>>>>>>>>>>>>>>>>>>>>>>>>') print( aQuestion ) print('<<<<<<<<<<<<<<<<<<<<<<<<') message = f"{HUMAN_PROMPT}{aQuestion}\n\n{AI_PROMPT}" client = Anthropic(api_key=getattr(settings, 'ANTHROPIC_API_KEY')) response = None try: response = client.completions.create( model="claude-2.1", prompt=message, max_tokens_to_sample=1000, ) response = response.completion retVal = COMMON.OK except Exception as e: print(f"> ERR: {str(e)}") retVal = COMMON.ERR print("--- %s seconds ---" % (time.time() - start_time)) return retVal, response