Deep Learning Projects

Welcome to the Deep Learning Projects repository. This repository contains multiple Streamlit-based projects showcasing applications of deep learning and machine learning in various domains.

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Projects Included

1. Movie Recommendation System

• Description: Recommends movies similar to the one provided by the user.

• Repository: Movie_Recommendation_System

• Instructions: Follow the project-specific README for setup and usage details.

• Required Files: Download supporting models and data from Google Drive under the Movie Recommendation System folder.

• Screenshot:

  

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2. Potato Leaf Disease Prediction

• Description: Predicts diseases in potato leaves based on an uploaded image.

• Repository: Potato_Leaf_Disease_Prediction

• Instructions: Follow the project-specific README for setup and usage details.

• Required Files: Download the pre-trained model and datasets from Google Drive under the Potato Leaf Disease Prediction folder.

• Screenshot:

  

  

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3. Cat/Dog Classifier

• Description: Classifies uploaded images as either a cat or a dog.

• Repository: Cat_Dog_Classifier

• Instructions: Follow the project-specific README for setup and usage details.

• Required Files: Download the model.h5 file from Google Drive under the Cat Dog Classifier folder.

• Screenshot:

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Getting Started

Prerequisites

1. Python 3.7 or later.
2. Install required dependencies by running the following command:

   pip install -r requirements.txt

General Instructions

Each project has its own directory with a README.md file explaining setup instructions, required files, and usage. Follow the steps mentioned to get each project running.

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File Structure

Deep-Learning-Projects/
├── Movie_Recommendation_System/
├── Potato_Leaf_Disease_Prediction/
├── Cat_Dog_Classifier/

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Additional Information

The models and datasets required for these projects are hosted on Google Drive due to size constraints. Please download the necessary files from this link and place them in their respective project folders as instructed.

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Acknowledgments

• TensorFlow, Keras, and Scikit-learn for their frameworks.
• Streamlit for providing an interactive and user-friendly platform for web apps.

Feel free to contribute or report issues! 🎉