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Rajdeep Singh
Rajdeep Singh

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Steps To Develop Your Own AI Astrology App

#machinelearning #development #ai

The demand for astrology apps is soaring. People are no longer satisfied with generic horoscopes—they want predictions that feel unique to them, based on their birth chart, past events, and personal circumstances. AI enables exactly that. With the ability to process large datasets, recognize patterns, and generate predictions, AI allows astrology apps to deliver customized readings with impressive accuracy.

The question is no longer if you should build an AI astrology app it’s how fast you can get to market with one that stands out.

In This Blog I will cover Backend Architecture and API Integration:

RESTful APIs for AI Model Communication
Frontend communicates with AI models via RESTful APIs. Endpoints include /generate-horoscope, /get-birth-chart, /analyze-compatibility. Use FastAPI or Flask for deployment, with JSON payloads for requests and responses.

Sample API call:

python

POST /generate-horoscope
{
  "birth_date": "1993-07-15",
  "birth_time": "11:30",
  "location": "Los Angeles, CA"
}
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Response:
json

{
  "daily_prediction": "Mars in Leo lights up your 10th house—speak up at work."
}
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Secure User Authentication

Use:

OAuth 2.0 (Google, Apple login)
Firebase Authentication (quick and secure)
Two-factor authentication (optional but powerful)
Encrypt all data using HTTPS, and use JWT tokens for session management.

Respect user privacy:

Allow users to delete their data
Offer anonymous mode
Explain what you track and why

Coding Example: AI Horoscope Generator using Python

Install packages:
Bash

pip install flatlib
pip install transformers
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Step 1: Generate the Birth Chart

from flatlib.chart import Chart
from flatlib.datetime import Datetime
from flatlib.geopos import GeoPos

# User input
birth_date = '1993-07-15'
birth_time = '11:30'
location = GeoPos('34.0522', '-118.2437')  # Los Angeles

# Combine date and time
datetime = Datetime(birth_date, birth_time, '+00:00')  # UTC

# Generate chart
chart = Chart(datetime, location)

# Get sun and moon placements
sun = chart.get('SUN')
moon = chart.get('MOON')
print(f"Sun is in {sun.sign}, Moon is in {moon.sign}")
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Step 2: Basic Interpretation Logic

sun_traits = {
   'CANCER': 'You are deeply intuitive and emotional.',
   'LEO': 'You are confident, proud, and love to shine.',
   # add more signs...
}

horoscope = sun_traits.get(sun.sign, 'Unique energy defines you.')
print(horoscope)
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Step 3: Generate Natural Text Using AI

from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")

prompt = f"Write a horoscope for someone with Sun in {sun.sign} and Moon in {moon.sign}: {horoscope}"

result = generator(prompt, max_length=60, do_sample=True)
print(result[0]['generated_text'])
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Sample Output from the AI

Here’s an example output for Sun in Cancer and Moon in Taurus:

Prompt: “Write a horoscope for someone with Sun in CANCER and Moon in TAURUS: You are deeply intuitive and emotional.”

Output: “Today, you may feel especially drawn to comfort and home. Your emotional sensitivity helps you connect with others on a deep level. Trust your instincts—they will guide you toward meaningful encounters.”

Use this template and improve it by fine-tuning models or combining it with larger horoscope datasets.

Testing and Quality Assurance

Unit Testing AI Outputs

def test_sun_sign_output():
    chart = Chart(Datetime('1993-07-15', '11:30', '+00:00'), GeoPos('34.0522', '-118.2437'))
    sun = chart.get('SUN')
    assert sun.sign == 'CANCER'
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UI/UX Testing

Test navigation ease, onboarding clarity, and device responsiveness using tools like BrowserStack or TestFlight.

Feedback Integration

Add thumbs-up/down ratings, "Was this helpful?" buttons, and feedback forms. Track usage patterns and engagement to improve personalization.

Deployment and Maintenance

Using Hugging Face for AI Hosting

Host models on Hugging Face Hub with InferenceApi. Example:

from huggingface_hub import InferenceApi

inference = InferenceApi(repo_id="your-username/astro-model")
response = inference("Sun in Leo and Moon in Aries")
print(response)
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Cloud-based Deployment
Use AWS EC2, Google Cloud App Engine, or Heroku. Employ Docker containers and CI/CD pipelines for reliability.

Continuous Model Updates

Update datasets regularly, retrain monthly, and A/B test new models. Log errors and unhelpful responses for refinement.

Sill Consultation Required: Read More

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