Demo Video Link: https://youtu.be/8s3sAIvPJv0?feature=shared
Step-by-Step Guide to Setting Up and Running the Application
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Clone the Repository
https://github.com/gourab9817/Flipkart_grid_6.0.git
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Create a Virtual Environment
python -m venv Grid
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Activate the Virtual Environment
- On Windows:
Grid\Scripts\activate
- On Linux/macOS:
source Grid/bin/activate
- On Windows:
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Install Required Packages
pip install -r requirements.txt
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Run the Application
python manage.py runserver
- Predicts the freshness and identifies the class (type) of fruits and vegetables.
- Utilizes a model I trained using a dataset from Kaggle with MobileNetV2 as the base model in TensorFlow.
- Extracts product details such as MRP, EAN, manufacture date, and expiry date using OCR powered by Pytesseract.
- Processes the text to validate the expiry date of the product.
Download the dataset from Kaggle.
- Link : https://www.kaggle.com/datasets/muhriddinmuxiddinov/fruits-and-vegetables-dataset product dataset- https://www.kaggle.com/datasets/gourab8889/product-detection-and-identification-dataset This dataset contains images of the following fruits and vegetables items:
Fresh fruits- fresh banana, fresh apple, fresh orange, fresh mango and fresh strawberry.
Rotten fruits- rotten banana, rotten apple, rotten orange, rotten mango and rotten strawberry.
Fresh vegetables- fresh potato, fresh cucumber, fresh carrot, fresh tomato and fresh bell pepper.
Rotten vegetables- rotten potato, rotten cucumber, rotten carrot, rotten tomato and rotten bell pepper.
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Tesseract : https://github.com/tesseract-ocr/tesseract
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Pytesseract : https://pypi.org/project/pytesseract/