Build a Real-Time Data Assistant

import requests
import json
import openai

# Step 1: Receive Request (Assuming we have location)
location = "Austin, TX"  # Example location

# Step 2: Call Dappier API for Weather
weather_query = f"What is the weather in {location} today?"
dappier_url = "https://api.dappier.com/app/aimodel/am_01j06ytn18ejftedz6dyhz2b15"
headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer <YOUR_DAPPIER_API_KEY>'
}

weather_payload = json.dumps({"query": weather_query})
weather_response = requests.post(dappier_url, headers=headers, data=weather_payload)
weather_data = weather_response.json().get('results')

# Step 3: Call Dappier API for Latest News
news_query = f"What is the latest news in {location} today?"
news_payload = json.dumps({"query": news_query})
news_response = requests.post(dappier_url, headers=headers, data=news_payload)
news_data = news_response.json().get('results')

# Step 4: Prepare ChatGPT Prompt
prompt_template = f"""
You are an Assistant who responds to requests first thing in the morning. Your job is to greet the user and provide them with the latest weather and news. Make the response playful, like a text message, and keep it short. Add a joke to brighten their day and ask a follow-up question.

Weather: {weather_data}
News: {news_data}
"""

# Step 5: Call ChatGPT API
openai.api_key = '<YOUR_OPENAI_API_KEY>'

response = openai.Completion.create(
    engine="text-davinci-003",
    prompt=prompt_template,
    max_tokens=150
)

chatgpt_response = response.choices[0].text.strip()

# Step 6: Return the ChatGPT Response
api_response = {
    "Response": chatgpt_response
}

print(json.dumps(api_response, indent=2))