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h_ai_claude.py
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# -*- 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