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from db import fetch_db_rows_as_dicts
import google.generativeai as genai
import json
GOOGLE_API_KEY='AIzaSyC0T2fN5Dga-6nkPc6HYV7-bDZskqgALX0'
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"{d} "
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:
# 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 findTop3MoneyNeeds(proposition):
moneyNeeds, rows = fetch_db_rows_as_dicts('money_needs.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 needDictIndexes
def findTop3Needs(proposition, moneyNeeds):
moneyNeedsString = concatenate_keys(moneyNeeds)
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']
#findTop3MoneyNeeds('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')
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