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Movie-Recommendation-System-using-Palm-2

Google Hackathon

Introduction

This Movie Recommendation System takes in the name of a movie and recommends similar movies based on the dataset. The recommendation process is powered by the google. generative-ai(presumably a mock-up API for the purpose of this example), which utilises a generative model to provide movie suggestions.

Requirements

  • Python libraries:
    • os
    • google.generativeai (You need an API key for this)
    • ipywidgets
    • pandas
    • IPython
  • Data: A CSV file named IMDBDataset.csv containing a list of movie names.

Setup

  • Set your PALM API key:
You can either set it as an environment variable or directly in the code.
python
Copy code: PALM_API_KEY = os.getenv("PALM_API_KEY", "YOUR_API_KEY_HERE")
  • Load the movie dataset:
The IMDBDataset.csv file is loaded into a pandas dataframe and then converted to a list of movie names.

Usage

  • Create an instance of the Recommend_movies class.
  • Use the generate method to get movie recommendations.
  • The interface uses IPython widgets for a simple GUI where users can input a movie name and get recommendations.

Workflow

  • The Recommend_movies class configures the generative AI model with the specified parameters.
  • Upon providing a movie name and pressing the button, the model takes a sample prompt with movie names and their corresponding recommendations.
  • Based on this prompt, the model tries to predict recommendations for the input movie.
  • The generated recommendations are then cross-referenced with the dataset to ensure they are valid.
  • The results are displayed using IPython.

Note

  • The safety_settings in the Recommend_movies class aims to prevent any inappropriate or harmful content from being generated.
  • If the movie name is not found, the system will prompt the user to try a different name.

Improvements

  • A more extensive dataset will provide better recommendations.
  • Fine-tuning the model specifically for movie recommendations can yield better results.
  • Incorporate user feedback to continually improve the recommendation quality.

Conclusion

This recommendation system provides a quick and easy way to find similar movies. With the potential to integrate more advanced features and datasets, this system can be further enhanced to suit various user needs.

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