JarvisAI

Last Updated: 30 May, 2021

1. What is Jarvis AI?
2. Prerequisite
3. Getting Started- How to use it?
4. How to contribute?
5. Future?

1. What is Jarvis AI-

Jarvis AI is a Python Module which is able to perform task like Chatbot, Assistant etc. It provides base functionality for any assistant application. This JarvisAI is built using Tensorflow, Pytorch, Transformers and other opensource libraries and frameworks. Well, you can contribute on this project to make it more powerful.

This project is crated only for those who is having interest in building Virtual Assistant. Generally it took lots of time to write code from scratch to build Virtual Assistant. So, I have build an Library called "JarvisAI", which gives you easy functionality to build your own Virtual Assistant.

Check more details here: https://github.com/Dipeshpal/Jarvis_AI

Check official website here: https://jarvis-ai-api.herokuapp.com/

API Documentations: https://jarvis-ai-api.herokuapp.com/api_docs/

2. Prerequisite-

• To use it only Python (> 3.6) is required.
• To contribute in project: Python is the only prerequisite for basic scripting, Machine Learning and Deep Learning knowledge will help this model to do task like AI-ML. Read How to contribute section of this page.

3. Getting Started (How to use it)-

Install the latest version-

pip install JarvisAI

It will install all the required package automatically.

If anything not install then you can install requirements manually. pip install -r requirements.txt The requirementx.txt can be found here.

https://pypi.org/project/JarvisAI/

Usage and Features-

After installing the library you can import the module-

Example-

1. Basic Usages: https://github.com/Dipeshpal/Jarvis-Assisant/blob/master/scripts/main.py

2. Advance Usages (Wake up using Hand Gesture): https://github.com/Dipeshpal/Jarvis-Assisant/blob/master/scripts/main_advance_usages.py

import JarvisAI
obj = JarvisAI.JarvisAssistant(sync=True, token='5ec64be7ff718ac25917c198f3d7a4')  # or JarvisAI.JarvisAssistant(sync=False) 
response = obj.mic_input_ai() # mic_input() can be also used
print(response)

Check this script for more examples- https://github.com/Dipeshpal/Jarvis-Assisant/blob/master/scripts/main.py

Available Methods-

The functionality is cleared by methods name. You can check the code for example. These are the names of available functions you can use after creating JarvisAI's object-

import JarvisAI
obj = JarvisAI.JarvisAssistant(sync=True, token='5ec64be7ff718ac25917c198f3d7a4') # or JarvisAI.JarvisAssistant(sync=False) 
response = obj.mic_input_ai()  # mic_input() can be also used

Note: First of all setup initial settings of the project by calling setup function.

res = obj.setup()

1. res = obj.mic_input(lang='en')

2. res = obj.mic_input_ai(record_seconds=5, debug=False)

3. res = obj.website_opener("facebook")

4. res = obj.send_mail(sender_email=None, sender_password=None, receiver_email=None, msg="Hello")

5. res = obj.launch_app("edge")

6. res = obj.weather(city='Mumbai')

7. res = obj.news()

8. res = obj.tell_me(topic='tell me about Taj Mahal')

9. res = obj.tell_me_time()

10. res = obj.tell_me_date()

11. res = obj.shutdown()

12. res = obj.text2speech(text='Hello, how are you?', lang='en')

13. res = obj.datasetcreate(dataset_path='datasets', class_name='Demo', haarcascade_path='haarcascade/haarcascade_frontalface_default.xml', eyecascade_path='haarcascade/haarcascade_eye.xml', eye_detect=False, save_face_only=True, no_of_samples=100, width=128, height=128, color_mode=False)

14. res = obj.face_recognition_train(data_dir='datasets', batch_size=32, img_height=128, img_width=128, epochs=10, model_path='model', pretrained=None, base_model_trainable=False)

15. res = obj.predict_faces(class_name=None, img_height=128, img_width=128, haarcascade_path='haarcascade/haarcascade_frontalface_default.xml', eyecascade_path='haarcascade/haarcascade_eye.xml', model_path='model', color_mode=False)

16. res = obj.setup()

17. res = obj.show_me_my_images()

18. res= obj.show_google_photos()

19. res = obj.tell_me_joke(language='en', category='neutral')

20. res = obj.hot_word_detect(lang='en')

21. status, response = obj.get_user_data(token="436c57eab581dbb2253cfa77c41574f6") # get your token from https://jarvis-ai-api.herokuapp.com/

22. obj.set_user_data()

23. obj.get_user_data()

24. obj.jarvisai_configure_hand_detector(camera=0, detectionCon=0.7, maxHands=2, cam_display=True, cam_height=480, cam_width=888)

25. jarvisai_detect_hands(self, message="")

4. How to contribute?

1. Clone this reop

2. Create virtual environment in python.

3. Install requirements from requirements.txt. pip install requirements.txt

4. Now run, __ init__.py python __init__.py and understand the working.

   Guidelines to add your own scripts / modules- Lets understand the projects structure first-

   JarvisAI:.
   ├───configs
   ├───features
   │   ├─── date_time
   │   │   └───...
   │   └───weather
   │       └───...
   └───...
   

   4.1. All these above things are folders. Lets understand-

   • JarvisAI: Root folder containing all the files

   • features: All the features supported by JarvisAI. This 'features' folder contains the different modules, you can create your own modules. Example of modules- "weather", "setup". These are the two folders inside 'features' directory.

   • __ init__.py: You need to run this file to test it during the production.

   4.2. You can create your own modules in this 'features' directory.

   4.3. Let's create a module and you can learn by example-

   • 4.3.1. We will create a module which will tell us a date and time.

   • 4.3.2. Create a folder (module) name- 'date_time' in features directory.

   • 4.3.3. Create a python script name- 'date_time.py' in 'date_time' folder.

   • 4.3.4. Write this kind of script (you can modify according to your own script). Read comments in script below to understand format-

     'features/date_time/date_time.py' file- Make sure to add docs / comments. Also return value if necessary.

      import datetime  
     
      def date():  
          """  
          Just return date as string  :return: date if success, False if fail  
          """  
          try:  
              date = datetime.datetime.now().strftime("%b %d %Y")  
          except Exception as e:  
              print(e)  
              date = False  
      	return date  
        
        
      def time():  
          """  
      	Just return date as string  :return: time if success, False if fail  
      	"""
      	try:  
              time = datetime.datetime.now().strftime("%H:%M")  
          except Exception as e:  
              print(e)  
              time = False  
      	 return time
     
     
      # you can run and test your script by calling from main
      if __name__ == '__main__':
          response = date()
          print(response)
          response = time()
          print(response)
     
     

   • 4.3.4. Integrate your module to Jarvis AI-

     • Open JarvisAI\JarvisAI\__init__.py

     • Format of this py file-

       # import custom features
       try:
       	import features.date_time.date_time
       except:
       	from .features.date_time import date_time
       
       # integrate your features
       class JarvisAssistant:  
           def __init__(self):  
               pass
       	def tell_me_date(self):  
       		return date_time.date()  
       		  
       	def tell_me_time(self):  
       		return date_time.time()
       
       # test your features from main
       if __name__ == '__main__':  
           obj = JarvisAssistant()		
           res = obj.tell_me_time()  
       	print(res)
       	res = obj.tell_me_date()
       	print(res)
       
       

4.4. That's it, if you applied all the things as per as guidelines then now just run __ init__.py it should works fine.

4.5. Push the repo, we will test it. If found working and good then it will be added to next PyPi version.

Next time you can import your created function from JarvisAI Example: import JarvisAI.tell_me_date

5. Future?

Lots of possibilities, GUI, Integrate with GPT-3, support for android, IOT, Home Automation, APIs, as pip package etc.

Todo list-

5.1. More API features

5.2. You tell me

FAQs for Contributors-

1. What I can install? Ans: You can install any library you want in your module, make sure it is opensource and compatible with win/linux/mac.

2. Code format? Ans: Read the example above. And make sure your code is compatible with win/linux/mac.

3. What should I not change? Ans: Existing code.

4. Credits- Ans: You will definitely get credit for your contribution.

5. Note- Ans: Once you created your module, test it with different environment (windows / linux). Make sure the quality of code because your features will get added to the JarvisAI and publish as PyPi project.

6. Help / Contact? Ans. Contact me on any of my social media or Email.

Let's make it big.

Feel free to use my code, don't forget to mention credit. All the contributors will get credits in this repo.