Complete ChatBot – automated and personalized attendant tool

Complete ChatBot is an automated and personalized attendant tool to better serve your customers or their needs. Meet your messaging demand 24 hours a day, with no monthly fees or additional costs. The tool allows you to create a fully customized menu of options, sending offers in image and text format, sending media files, sending job offers, pre-defined messages and much more…
Get a version now and simplify customer service!

Chat Bot Features :-

  1. Version compiled with installer.
  2. Multiple Languages.
  3. Available in English, Spanish, Russian, Arabic and Portuguese – Brazil.
  4.  Send Static Text.
  5.  Send Offers (Image and Text).
  6.  Send Job Openings.
  7. Send image, video, audio, pdf and other files.
  8.  Send contacts from the phonebook.
  9.  Multiple instances.
  10. Dynamic Menu Creation.
  11.  Create menu and submenu..
  12.  Save Customer Registration.
  13.  Import and Export Customers.
  14.  Import and Export vcard.
  15.  SQLite Data Base.
  16.  Clean and Organized Code.
  17. License Generator (Extended license only).
  18.  C# Source Code (Extended license only).

 

Requirements :-

  1. Windows 7 to 10 (Personal computer).
  2. Windows Server 2012 to 2022 (VPS Server).
  3. Visual Studio 2017 to 2022.
  4. Minimum Hardware 2 Core and 2 GB of Ram.
  5. Install .Net Framework 4.6.1.
  6. Install Google Chrome.
  7. Knowledge in C# Forms (Extended license only).
  8. Knowledge in SQLite (Extended license only).
  9. Normal or Business WhatsApp Account.

For Live Demo & Enquiry  :

Call/WhatsApp : +916263056779

Email : official@projectworlds.in

Script Come With :

  • Free Installation support
  • Free technical support
  • Future product updates
  • Quality checked by PROJECTWORLDS
  • Lowest price guarantee
  • 3 months support included

Multi Vendor ebook Android App (Paid book app, PDF, ePub, payment gateway) + admin panel

Multi-Vendor ebook Android App (Paid book app, PDF, ePub, payment gateway) + admin panel + author panel

Multi-Vendor ebook Android Application to read the books & magazine online. User can register, login , read the ebook & emagazine, buy paid ebook, download ebooks to read offline, rate ebooks, comments ebooks, search ebooks share app via social media apps and many more features within the app. In Multi-Vendor ebook Android App, User can buy paid book via Google In-App Purchase, paypal , Razorpay, Paytm, Flutterwave, PayUmoney, UPI payment gateway & read , download book. also user login with facebook, login with gmail & OTP registration

User can register as Author & after Approval admin, Author can upload unlimited Free/Paid books and Earning money user bought books. Author can manage books/magazine from app & author panel. also check earning, download books count, payment history, Sales Reports, Bank Details. Author will get books sell payment at end of month. User can buy a book and also buy a chapters

Multi-Vendor ebook Android App – contains Books & Magazines also nicely manage wallet in application. Wallet history & Transacation history show on wallet page.

Application have clean source code, buyer will get nice documentations for reskin the app and upload admin panel on server, Its Easy to Reskin& customization

Features Application for Multi-Vendor ebook Android App:-

  1. User become Author
  2. Author can Upload books/Magazine from Application
  3. Payment Gateway manage from admin panel
  4. Top Reading, Popular, New Arrival Books
  5. Search books and authors
  6. Author bio and its books
  7. Top Categories
  8. Books Collection by Categories
  9. List of authors
  10. List of Paid Books
  11. List of Free Books
  12. Continue Reading Books
  13. Book Detail & Related Books
  14. Awesome material Design
  15. Add Comments
  16. Sample Book for Read before Buy
  17. Buy Paid Book
  18. Google in-app purchase integrated
  19. Paypal Payment Gateway integrated
  20. RazorPay Payment Gateway integrated
  21. Paytm integrated
  22. Flutterwave integrated
  23. PayUmoney integrated
  24. Support PDF / EPUB files
  25. PDF Horizontal view
  26. Free Book Direct Download
  27. User Purchased books Collection
  28. User Profile
  29. Add to Bookmark
  30. user login / registration
  31. Settings
  32. Enable push notification
  33. Share App
  34. Rate App
  35. Logout
  36. Screenshot disable
  37. Share Books with others
  38. Download Books for offline
  39. Downloaded Books with password
  40. advertise banner show
  41. Beautiful UI / UX
  42. Support user account
  43. Admob Banners and Interstitial Ads
  44. OneSignal Push Notification
  45. All Device Compatibility
  46. Latest UI With Material Design
  47. Easy to Reskin
  48. Easy to Customization
  49. Night Mode Support
  50. RTL Support
  51. Multilanguage Support
  52. Privacy policy page added
  53. About Us page added
  54. Download books in private storage
  55. Night mode available
  56. Login with facebook
  57. Login with Gmail
  58. Login with Mobile OTP

Features Admin Panel for Multi-Vendor ebook Android App:-

  1. Simple, Attractive & Statistical Dashboard
  2. Manage Books with Categories and Author
  3. Add/Update/Delete Categories
  4. Add/Update/Delete Authors
  5. Add/Update/Delete Books
  6. Add/Update/Delete Magazine
  7. Manage Comments by admin
  8. Manage App Setting from admin panel
  9. Sales Report
  10. Settlement – author payment
  11. Payout History
  12. Change Currency from admin panel
  13. SMTP Intigrated
  14. Receive Email when book purchase
  15. Admob on/off from backed
  16. Push Notification send from admin panel
  17. Push Notification key chage from backend
  18. Attractive UI / UX
  19. Advertise Banner
  20. Easy to Reskin
  21. Easy to Customization

Features Author Panel for Android EBook App:-

  1. Simple, Attractive & Statistical Dashboard
  2. Manage Books with Categories and Author
  3. Add/Update/Delete Books
  4. Payout Report
  5. Sales Report
  6. Profile
  7. Bank Details
  8. SMTP Intigrated
  9. Receive Email when book purchase
  10. Attractive UI / UX
  11. Easy to Reskin
  12. Easy to Customization

Server Requirements:-

  1. PHP Vertsion 5.6/5.8/7.0
  2. Apache server
  3. MySQL Database
  4. Hosting with cpanel/WHM recommended

What You Get:-

  1. Full Android Source Code
  2. Full Php Code of Server Side
  3. Well Documentation

For Live Demo & Enquiry  :

Call/WhatsApp : +916263056779

Email : official@projectworlds.in

Script Come With :

  • Free Installation support
  • Free technical support
  • Future product updates
  • Quality checked by PROJECTWORLDS
  • Lowest price guarantee
  • 3 months support included

Music Recommendation Based on Facial Expression Sporify api Python

The emotion recognition model is trained on FER 2013 dataset. It can detect 7 emotions. The project works by getting live video feed from web cam, pass it through the model to get a prediction of emotion. Then according to the emotion predicted, the app will fetch playlist of songs from Spotify through spotipy wrapper and recommend the songs by displaying them on the screen.

Features:

  • Real time expression detection and song recommendations.
  • Playlists fetched from Spotify using API.
  • Neumorphism UI for website.

Installation Steps:

Flask:

  • Run pip install -r requirements.txt to install all dependencies.
  • In Spotipy.py enter your credentials generated by your Spotify Developer account in 'auth_manager'. Note: - This is only required if you want to update recommendation playlists. Also uncomment import statement in 'camera.py'.
  • Run python app.py and give camera permission if asked.

Technology Overview:

  1. Keras
  2. Tensorflow
  3. Spotipy
  4. flask
  5. Flask

Dataset:

The dataset used for this project is the famous FER2013 dataset. Models trained on this dataset can classify 7 emotions. The dataset can be found here.

Note that the dataset is highly imbalanced with happy class having maxiumum representation. This might be a factor resulting in okaysish accuracy after training.

Model Architecture:

  • The model architecture is a sequential model consisting of Conv2d, Maxpool2d, Dropout and Dense layers:
  1. Conv2D layers throughout the model have different filter size from 32 to 128, all with activation 'relu'
  2. Pooling layers have pool size (2,2)
  3. Dropout is set to 0.25 as anything above results in poor performance
  4. Final Dense layer has 'softmax' activation for classifying 7 emotions
  • Used 'categorical_crossentropy' for loss with 'Adam' optimizer with 'accuracy' metric

Note:- Tried Implementing various other models like VGG16 but accuracy was far too low. This model architecture gives good enough accuracy. A bit more tinkering with hyper parameters might lead to a better accuracy

Image Processing and Training:

  • The images were normalised, resized to (48,48) and converted to grayscale in batches of 64 with help of 'ImageDataGenerator' in Keras API.
  • Training took around 13 hours locally for 75 epochs with an accuracy of ~66 %

Issue:

The app in current state can't be deployed on web as:

  1. Opencv tries to open the camera on whatever device the app is running on. Code in current state makes use of webcam if available on server side not client side. So when app is run locally on a laptop Video Streaming through webcam is possible. But if it's deployed to a cloud, the app is stored in a data center somewhere which obviously doesn't have web camera connected to it and hence it doesn't work.

Further Work:

  1. Instead of CSVs, create a databse and connect it to application. The DB will fetch songs for recommendations and new songs can be updated directly onto database
  2. Add a feature which will update specified playlists for better and more recent recommendations, a specific day over a fixed duration say every sunday and append it to database
  3. Directly play the song or redirect to the song on Spotify when user clicks on it.
  4. Rewrite code such that Video Streaming is done on client side instead of server side so as it make the app deployable

Note: Model accuracy is not that great. It is ~66%. Further training and finetuning required. May try Vision Transformer Model.

Download Link

Object detection Python Machine Learning Web App

Buy Now ₹1501

Buy Now Project Report ₹1001

This Project is a web application built with the python-flask framework, that uses YOLO weights to detect the Objects. Using YOLO object detection algorithm.

YOLOv5  is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

YOLO an acronym for 'You only look once', is an object detection algorithm that divides images into a grid system. Each cell in the grid is responsible for detecting objects within itself.

YOLO is one of the most famous object detection algorithms due to its speed and accuracy.

How Does it Work

To put it simply, the back-end receives an image from a user and runs an object detection algorithm on the image (YOLO v3). Once the predictions are obtained, they are drawn on the image, which is, then, sent back to the user (to the front-end). In this README I provided the environment setup for the computing machine, which runs the detection algorithm. However, setting up the back-end machine is just the tip of an iceberg. The whole engineering pipeline includes many other steps full of caveats. If you are interested in the details of each step, checkout How Did You Build Your Object Detector?.

Technology Used in the project :-

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. Flask: Project has been developed over the Flask Framework

Supported Operating System :-

  1. We can configure this project on following operating system.
  2. Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  3. Python 3.7, PIP, Django.
  4. Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Step : -

  1. python 3.7.0
  2. command 1 - python -m pip install --user -r requirements.txt
  3. command 2 - python webapp.py

 

Food recommends based on your mood python django

It recommends food based on your mood. Also, it orders the recommendation list of restaurants using machine learning combining the user ratings, distance and cost for two, hence offering the most optimal choice.

This is a web application which recommends you food based on your mood using machine learning and delivers it to your doorstep.

We understand that the first thought that comes to our mind when we come across a food suggesting and delivery app, is obviously- "HOW IS THIS SOMETHING NEW?"

We'll explain. This web application is based on an absolutely new idea and is completely different from the traditional food delivery websites.

Firstly, it suggests a list of restaurants combining three basic parameters i.e cost, user rating and distance, all at the same time. Hence, it offers you the most optimal choice available, saving you the trouble of choosing the best restaurant for you using individual filters on traditional apps. The most commonly used apps allow you to choose a restaurant on the basis of only a single critera at a time, but we recommend you the nearest, most affordable and best quality restaurants, thereby catering to all your demands at the same time without having you to compromise on any of them.

Secondly, we understand that there are times when you are unable to decide what to order. Infact, we have personally faced this dilemma a hundred times when we've spent hours scrolling a food delivery app, thinking about the most important question which seems to have been given the least importance uptil now , "WHAT TO EAT?" MoodieFoodie helps you to choose a food item for all your moods, be it a happie, a sadie, an angrie, a dehydratie, a depressie, an excitie or an unwellie mood ;") Whatever be your mood, we can always suggest you food!

The app uses previous user moods and their respective food orders to suggest the best food choice for you.

Thirdly, we offer several other options for food ordering like the list of food items ordered the most at a particular time of the day or the restaurants that can order food in the minimum time if you're really hungry or the top searched food items, etc.

And lastly, this app allows you to order the food items that traditional food delivery apps don't. You can order tea, juices or even chocolates. For this, we have a separate "Wanna sell on MoodieFoodie" option for small scale sellers like small tea shops, women who make homemade chocolates or other small scale setups who wish to sell online.

Working and Implementation

  • The major feature of our project which sets it apart from the traditional apps of its kind needs three parameters to order the list of recommended restaurants with the highly recommended restaurant at the top. These three features are the user ratings, cost for two people and the restaurant's distance from the user.
    • Whenever any user orders from a certain restaurant, he/she is asked to rate the restaurant out of 5. The rating of that restaurant is then dynamically updated using the mathematical average of the current rating and the new rating. This computation also requires the storage of the number of ratings for a particular restaurant.
    • The cost for two people is permanently stored initially when a restaurant is added to the database.
    • To compute the distance of the user from a restaurant, we have stored the geocode of all the restaurants in the database. We have used the Google Maps API to extract the user's geocode and hence compute his/her distance.

    A very simple model of machine learning, that is logistic regression is used to implement the same. When the user chooses a certain restaurant among the recommended restaurants, then his/her choice to select a certain restaurant and discard other restaurants is used to train the model which only increases the accuracy of the algorithm.

  • The USP of our project, which is recommending the users food items based on their mood works on previous users' inputs only. Whenever any user orders from a certain restaurant, he/she is asked to specify what his/her mood was before he/she had ordered. His/Her order and mood is used to train the algorithm which gradually increases its accuracy.
  • To help small businesses expand their reach by selling on our application, we have simply provided them with a form wherein they can fill out the necessary details.

Installation Requirements

  1. Framework : Django, Version : 1.11.17
  2. Language : Python, Version : 3.6.3
  3. To run it, you need to install some packages and libraries as follows:
  4. Bootstrap 3
  5. numpy
  6. sklearn
  7. bcrypt
  8. django[argon]

Install dependencies directly from requirements.txt in pip installation,
To install these, write this on the command line terminal:
"pip install -r requirements.txt"

Run Project :-

 

  1. Enter command: "pip install -r requirements.txt"
  2. Enter the command: "python manage.py runserver"
  3. Copy the url and paste it in your favourite browser window.

Download Project