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| from db import fetch_db_rows_as_dicts | |
| import google.generativeai as genai | |
| import json | |
| import os | |
| import pandas as pd | |
| GOOGLE_API_KEY= os.getenv('GEMINI_API_KEY') | |
| genai.configure(api_key=GOOGLE_API_KEY) | |
| model = genai.GenerativeModel(model_name = "gemini-pro") | |
| def load_json_from_string(json_string): | |
| try: | |
| data = json.loads(json_string) | |
| return data | |
| except json.JSONDecodeError as e: | |
| print(f"Error decoding JSON: {e}") | |
| except Exception as e: | |
| print(f"An error occurred: {e}") | |
| def concatenate_keys(keys): | |
| concatenated_string = "" | |
| for i, d in enumerate(keys, start=1): | |
| concatenated_string += f"{i}. {d}" | |
| print('##########################') | |
| print(concatenated_string.strip()) | |
| return concatenated_string.strip() | |
| def transform_to_dict_of_dicts(columns, rows): | |
| # Initialize the result dictionary | |
| result = {} | |
| # Iterate over each row | |
| for row in rows: | |
| #print(dict(row)) | |
| # The first element of the row is the key for the outer dictionary | |
| outer_key = row[0].strip() | |
| # Initialize the inner dictionary | |
| inner_dict = {} | |
| # Iterate over the rest of the elements in the row | |
| for i, value in enumerate(row[1:], start=1): | |
| # The corresponding column name is the key for the inner dictionary | |
| inner_key = columns[i].strip() | |
| # Add the key-value pair to the inner dictionary | |
| inner_dict[inner_key] = value | |
| # Add the inner dictionary to the result dictionary with the outer key | |
| result[outer_key] = inner_dict | |
| return result | |
| def transform_topologies_to_dict(columns, rows): | |
| # Initialize the result dictionary | |
| result = {} | |
| # Iterate over each row | |
| for row in rows: | |
| #print(dict(row)) | |
| # The first element of the row is the key for the outer dictionary | |
| outer_key = row[0].strip() | |
| # Initialize the inner dictionary | |
| inner_dict = {} | |
| # Iterate over the rest of the elements in the row | |
| for i, value in enumerate(row[1:], start=1): | |
| # The corresponding column name is the key for the inner dictionary | |
| inner_key = columns[i].strip() | |
| # Add the key-value pair to the inner dictionary | |
| inner_dict[inner_key] = value | |
| # Add the inner dictionary to the result dictionary with the outer key | |
| result[outer_key] = inner_dict | |
| return result | |
| def listNeeds(tableName, dbName='data.sqlite'): | |
| needs, rows = fetch_db_rows_as_dicts(dbName, tableName) | |
| needsDict = transform_to_dict_of_dicts(needs, rows) | |
| return list(needsDict.keys()), needsDict | |
| def findTop3MoneyNeeds(proposition): | |
| moneyNeeds, rows = fetch_db_rows_as_dicts('data.sqlite', 'money_needs') | |
| moneyNeedsDict = transform_to_dict_of_dicts(moneyNeeds, rows) | |
| #print(list(moneyNeedsDict.keys())) | |
| needs = findTop3Needs(proposition, list(moneyNeedsDict.keys())) | |
| needDictIndexes = [] | |
| for need in needs: | |
| needDictIndexes.append(moneyNeedsDict[need]) | |
| #print(needDictIndexes) | |
| return needs, needDictIndexes | |
| def findTop3CustomerExperienceNeeds(proposition): | |
| moneyNeeds, rows = fetch_db_rows_as_dicts('data.sqlite', 'customer_exp') | |
| moneyNeedsDict = transform_to_dict_of_dicts(moneyNeeds, rows) | |
| #print(list(moneyNeedsDict.keys())) | |
| needs = findTop3Needs(proposition, list(moneyNeedsDict.keys())) | |
| needDictIndexes = [] | |
| for need in needs: | |
| needDictIndexes.append(moneyNeedsDict[need]) | |
| #print(needDictIndexes) | |
| return needs, needDictIndexes | |
| def findTop3SustainabilityNeeds(proposition): | |
| print(" Proposition sustain = {}".format(proposition)) | |
| allNeeds, rows = fetch_db_rows_as_dicts('data.sqlite', 'sustainability') | |
| needsDict = transform_to_dict_of_dicts(allNeeds, rows) | |
| needs = findTop3Needs(proposition, list(needsDict.keys())) | |
| needDictIndexes = [] | |
| print(list(needsDict.keys())) | |
| for need in needs: | |
| needDictIndexes.append(needsDict[need]) | |
| print(needDictIndexes) | |
| return needs, needDictIndexes | |
| def findTop3Needs(proposition, needs): | |
| needsString = concatenate_keys(needs) | |
| prompt = '''You have this comma separated listed needs of customers | |
| {} | |
| Now given a proposition | |
| "{}" | |
| Find the best 3 strings out of the above numbered list which best matches this proposition. Return in output only the number next to the matching string strictly only in json under a list called matches | |
| ''' | |
| needsPrompt = prompt.format(needsString, proposition) | |
| print(needsPrompt) | |
| response = model.generate_content([needsPrompt]) | |
| output = response.text | |
| output = output.replace('```json', '') | |
| output = output.replace('```', '') | |
| obj = load_json_from_string(output) | |
| print(obj) | |
| needsIndexes = [needs[int(idx)-1] for idx in obj['matches']] | |
| return needsIndexes #obj['matches'] | |
| def findTop3Topologies(proposition, demographic): | |
| topologies = pd.read_csv('topologies_desc.csv', encoding = "ISO-8859-1") | |
| topologies = topologies.dropna(axis=1, how='all') | |
| topologyAttributes = topologies['Column1'] | |
| topologyNames = list(topologies.columns) | |
| topologyNames.remove('Column1') | |
| #print(" topologyNames = {} ", topologyNames) | |
| topologyDetails = {} | |
| for name in topologyNames: | |
| topologyDetails[name] = {} | |
| for attribute in topologyAttributes: | |
| topologyDetails[name][attribute] = topologies[name][pd.Index(topologies['Column1']).get_loc(attribute)] | |
| prompt = '''You have these listed topology names of a demographic in comma separated values below | |
| {} | |
| Now for each of these above topologies here are the details | |
| {} | |
| Now given a proposition details below | |
| {} | |
| and given a demographic details below | |
| {} | |
| Find the best 3 common strings out of the topology names which matches the proposition and the demographic the most. Return output strictly only in json under a list called matches | |
| ''' | |
| topologyPrompt = prompt.format(", ".join(topologyNames), str(topologyDetails), proposition, demographic) | |
| response = model.generate_content([topologyPrompt]) | |
| output = response.text | |
| output = output.replace('```json', '') | |
| output = output.replace('```', '') | |
| obj = load_json_from_string(output) | |
| print(obj) | |
| return obj['matches'], topologyDetails | |
| def generatePropositionExample(productName, selectedProduct, moneyNeeds, customerExperience, sutainabilityNeeds): | |
| proposal = '''You are a business sales professional who can form propostion summary of 100 words based upon the details. | |
| Please take the below details and summarize a propostion in less than 100 words. | |
| product name = {} | |
| product type = {} | |
| money needs of customer which this product is supposed to target = {} | |
| Customer experience needs which our company will provide = {} | |
| Sustainability needs which our product takes care of = {} | |
| ''' | |
| proposal = proposal.format(productName, selectedProduct, moneyNeeds, customerExperience, sutainabilityNeeds) | |
| response = model.generate_content([proposal]) | |
| return response.text | |
| # def findTop3Needs(proposition, moneyNeeds): | |
| # moneyNeedsString = concatenate_keys(moneyNeeds) | |
| # print(moneyNeedsString) | |
| # prompt = '''You have these listed needs of customers | |
| # {} | |
| # Now given a proposition | |
| # "{}" | |
| # Find the best 3 strings out of the list which matches this proposition. Return output strictly only in json under a list called matches | |
| # ''' | |
| # moneyNeedsPrompt = prompt.format(moneyNeedsString, proposition) | |
| # response = model.generate_content([moneyNeedsPrompt]) | |
| # output = response.text | |
| # output = output.replace('```json', '') | |
| # output = output.replace('```', '') | |
| # obj = load_json_from_string(output) | |
| # print(obj) | |
| # return obj['matches'] | |
| # findTop3Topologies('We have a product for family people giving them discounts and low interest loans for home appliances. They can pay us back in small instalments over the course of 4 years', | |
| # 'CharlesTown city people are young families people mostly with a population of 20000. Out of this 65% are between the age of 30-45. Most of them have kids aged between 0-15') | |
| #findTop3SustainabilityNeeds('We support Home appliances are all electric and use no fuel based energy') | |
| #We provide a credit card which gives 10% discount on purchasing home appliances and also provides low interest rates based loans | |
| #customer need - We provide our customer with utmost comfort and at home service | |
| # subscriber take out | |
| # 250 and below with a negative factor of 2.0 | |
| # 260 with a negative factor of 1.8 | |
| # 270 with a negative factor of 1.6 | |
| # 280 with a negative factor of 1.0 | |
| # 300 with a factor of 1 | |
| # 310 with a factor of 1.2 | |
| # 320 with a factor of 1.4 | |
| # 340 with a factor or 1.5 | |
| # 360+ with a factor of 2.0 | |