Photo-Estimator

Ever Wonder whether an image you just took wil get as many views and likes as you think it would , will maybe not but major companies would for their marketing and business, PhotoEstimator is a web application that determines whether the photo you're going to upload on Instagram is likely to be a "good" one or not. By analyzing your past Instagram photos and their performance, it uses a Convolutional Neural Network (CNN) to predict the potential success of your new photo based on the number of likes it might receive.

Table of Contents

• Features
• How It Works
• Installation
• Usage
• Technologies Used
• Contributing
• License

Features

• Instagram Integration: Connect your Instagram profile to the web app.
• Photo Analysis: Scrapes and analyzes your past photos and likes.
• Predictive Modeling: Uses a CNN to predict the success of a new photo.
• User-Friendly Interface: Simple interface to upload and analyze photos.

How It Works

1. Instagram Profile Connection: The user enters their Instagram profile, and the web app sends a follow request using the Instagram API.
2. Data Scraping: The app scrapes the user's photos and gathers data on the number of likes each photo received.
3. Model Training: The application trains a Convolutional Neural Network (CNN) using the scraped photos and their like counts to understand patterns and features of popular photos.
4. Prediction: When the user wants to upload a new photo, the app predicts whether it will be a hit or not relative to the maximum and minimum number of likes received on the user's past photos.

Installation

Prerequisites

• Python 3.x
• Flask
• Instagram API credentials
• Machine Learning libraries (e.g., TensorFlow ,keras,sklearn)

Steps

1. Clone the repository:

   git clone https://github.com/Amine-Zitoun/Photo-Estimator.git
   cd Photo-Estimator

2. Set up the backend:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Set up Instagram API:

   • Obtain Instagram API credentials and set them up in the environment variables or a configuration file.

4. Run the application:

   flask run

Usage

1. Open your web browser and navigate to http://localhost:5000.
2. Enter your Instagram profile username.
3. Allow the app to send a follow request and scrape your photos.
4. Upload a new photo to see the prediction on its potential success.

Technologies Used

• Python: Backend processing and data handling.
• Flask: Web framework for the backend.
• Instagram API: To connect and scrape user data.
• TensorFlow/Keras: For training and using the Convolutional Neural Network.
• HTML/CSS: Frontend development.

Contributing

Contributions are welcome! Please fork this repository and submit a pull request for review.

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Description of your changes"

4. Push to the branch:

   git push origin feature-branch

5. Submit a pull request.