PersonalizedFinance

// pip install requests pandas

import requests
import json
import pandas as pd

# Replace with your Typeform API token and form ID
API_TOKEN = "your_typeform_api_token"
FORM_ID = "your_typeform_form_id"

# Function to fetch responses from Typeform
def get_typeform_responses():
    headers = {
        "Authorization": f"Bearer {API_TOKEN}"
    }
    
    url = f"https://api.typeform.com/forms/{FORM_ID}/responses"
    response = requests.get(url, headers=headers)
    
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to fetch data. Status code: {response.status_code}")

# Fetch responses
typeform_data = get_typeform_responses()

def parse_responses(typeform_data):
    parsed_data = []

    for response in typeform_data['items']:
        answers = {}
        for answer in response['answers']:
            # Example: Mapping question IDs to meaningful field names
            if answer['field']['id'] == "income_question_id":
                answers['income'] = float(answer['number'])  # Assuming the answer is a number
            elif answer['field']['id'] == "expenses_question_id":
                answers['expenses'] = float(answer['number'])
            elif answer['field']['id'] == "savings_goal_question_id":
                answers['savings_goal'] = float(answer['number'])
        
        parsed_data.append(answers)

    return pd.DataFrame(parsed_data)

# Parse responses into a DataFrame
df = parse_responses(typeform_data)
print(df.head())

def analyze_finances(df):
    # Calculate disposable income
    df['disposable_income'] = df['income'] - df['expenses']
    
    # Calculate the percentage of income saved towards the goal
    df['savings_percentage'] = (df['savings_goal'] / df['income']) * 100

    # Generate simple advice
    df['advice'] = df.apply(lambda row: generate_advice(row), axis=1)
    
    return df

def generate_advice(row):
    if row['disposable_income'] < 0:
        return "You are spending more than you earn. Consider cutting back on expenses."
    elif row['savings_percentage'] < 20:
        return "Try to save at least 20% of your income to secure your financial future."
    else:
        return "You're on track with your savings goal! Keep it up."

# Analyze the finances and generate advice
df_analysis = analyze_finances(df)
print(df_analysis)

def generate_report(df):
    reports = []
    
    for _, row in df.iterrows():
        report = f"""
        Personalized Finance Report:
        ---------------------------
        Income: ${row['income']}
        Expenses: ${row['expenses']}
        Disposable Income: ${row['disposable_income']}
        Savings Goal: ${row['savings_goal']}
        Savings Percentage: {row['savings_percentage']:.2f}%
        
        Financial Advice: {row['advice']}
        """
        reports.append(report)
    
    return reports

# Generate the report for all users
reports = generate_report(df_analysis)
for report in reports:
    print(report)

import requests
import pandas as pd

# Replace with your Typeform API token and form ID
API_TOKEN = "your_typeform_api_token"
FORM_ID = "your_typeform_form_id"

# Function to fetch responses from Typeform
def get_typeform_responses():
    headers = {
        "Authorization": f"Bearer {API_TOKEN}"
    }
    
    url = f"https://api.typeform.com/forms/{FORM_ID}/responses"
    response = requests.get(url, headers=headers)
    
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to fetch data. Status code: {response.status_code}")

# Parse the Typeform data into a DataFrame
def parse_responses(typeform_data):
    parsed_data = []

    for response in typeform_data['items']:
        answers = {}
        for answer in response['answers']:
            if answer['field']['id'] == "income_question_id":
                answers['income'] = float(answer['number'])
            elif answer['field']['id'] == "expenses_question_id":
                answers['expenses'] = float(answer['number'])
            elif answer['field']['id'] == "savings_goal_question_id":
                answers['savings_goal'] = float(answer['number'])
        
        parsed_data.append(answers)

    return pd.DataFrame(parsed_data)

# Analyze financial data
def analyze_finances(df):
    df['disposable_income'] = df['income'] - df['expenses']
    df['savings_percentage'] = (df['savings_goal'] / df['income']) * 100
    df['advice'] = df.apply(lambda row: generate_advice(row), axis=1)
    return df

def generate_advice(row):
    if row['disposable_income'] < 0:
        return "You are spending more than you earn. Consider cutting back on expenses."
    elif row['savings_percentage'] < 20:
        return "Try to save at least 20% of your income to secure your financial future."
    else:
        return "You're on track with your savings goal! Keep it up."

# Generate personalized finance reports
def generate_report(df):
    reports = []
    for _, row in df.iterrows():
        report = f"""
        Personalized Finance Report:
        ---------------------------
        Income: ${row['income']}
        Expenses: ${row['expenses']}
        Disposable Income: ${row['disposable_income']}
        Savings Goal: ${row['savings_goal']}
        Savings Percentage: {row['savings_percentage']:.2f}%
        
        Financial Advice: {row['advice']}
        """
        reports.append(report)
    return reports

# Fetch responses, parse data, analyze finances, and generate reports
typeform_data = get_typeform_responses()
df = parse_responses(typeform_data)
df_analysis = analyze_finances(df)
reports = generate_report(df_analysis)

# Print all reports
for report in reports:
    print(report)