dzakyputra

'use client';

import { useState } from 'react';

interface Message {
  role: 'user' | 'assistant';
  content: string;
}

export default function Home() {

dzakyputra

import { NextResponse } from 'next/server';

export async function POST(req: Request) {

    const { prompt } = await req.json();

    const response = await fetch('https://api.openai.com/v1/chat/completions', {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',

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query = """
        SELECT
            complete.offer_type,
            complete.complete_without_view_income,
            complete_view.complete_with_view_income
            
        FROM
            (SELECT
                offer_type,
                AVG(income) complete_without_view_income

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# Find the spending distribution
spending_distribution = transcript[transcript['event'] == 'transaction'].merge(profile, left_on='person', right_on='id')

# Histogram for the Male
plt.hist(spending_distribution[spending_distribution['gender'] == 'M']['amount'], 
         range=(0, 40), 
         alpha=0.5, 
         bins=40, 
         label='Male')

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# Calculate the average spending for each gender
avg_spending = transcript[transcript['event'] == 'transaction'].merge(profile, left_on='person', right_on='id') \
                                                               .groupby('gender', as_index=False)['amount'] \
                                                               .mean() \
                                                               .rename(columns={'amount': 'average_spending_per_transaction'}) \
                                                               .sort_values('average_spending_per_transaction')

avg_spending['gender'] = avg_spending['gender'].map({'F': 'Female', 'M': 'Male', 'O': 'Others'})

# Start the visualization process

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query = """
        SELECT
            complete.offer_type,
            complete.gender,
            complete.complete_without_view,
            complete_view.complete_with_view,
            (complete.complete_without_view + complete_view.complete_with_view) total_complete
            
        FROM
            (SELECT

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query = """
        SELECT
            complete.offer_id,
            complete.offer_type,
            total_completed,
            total_completed_without_view,
            ROUND(((1.0*total_completed_without_view) / (1.0*total_completed))*100, 2) as total_completed_without_view_ratio,
            100 - ROUND(((1.0*total_completed_without_view) / (1.0*total_completed))*100, 2) as total_completed_with_view_ratio,
            `loss ($)`
        FROM

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# Reference
offer_reference = {}
offer_duration_reference = {}

for i,j in zip(portfolio['id'], portfolio['offer_type']):
    offer_reference[i] = j

for i,j in zip(portfolio['id'], portfolio['duration']):
    offer_duration_reference[i] = j*24
    

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# Profile preprocessing

# Convert the became_member_on into datetime type
profile['became_member_on'] = pd.to_datetime(profile['became_member_on'], format='%Y%m%d')

# Create a new column with the value of the difference days between the column became_member_on and the max days
profile['difference_days'] = (profile['became_member_on'].max() - profile['became_member_on']).dt.days


## Find the median and mode

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## Transcript preprocessing

# Convert the dictionary value into columns and concatenate with the current dataframe
value = pd.io.json.json_normalize(transcript['value'])
transcript = pd.concat([transcript, value], axis=1).drop(columns=['value'])

# Merge the offer_id column and offer id collumn so that it only has one column
transcript['offer_id'] = np.where(pd.isnull(transcript['offer_id']), transcript['offer id'], transcript['offer_id'])
transcript.drop(columns=['offer id'], inplace=True)