IMDb-Clone

Commands to Start the Application:

• Run main_app.py
  • From the terminal: Run main_app.py.
  • Activate the virtual environment: .\venv\Scripts\activate.

Operating Systems Supported:

• Windows

Libraries To Install:

• ttkbootstrap
• pandas
• matplotlib
• fuzzywuzzy
• python-Levenshtein
• tkinter
• random
• datetime
• numpy

APIs Used:

• OMDb API: For movies data retrieval.
• TMDb API: For ratings retrieval.

Features Implemented:

• Login Page
• New Registration Page
• Search Bar
  • Displays user info upon click.
• Main Page
  • Shows top 12 movies.
  • Clicking on any movie displays:
    • Plot
    • Director
    • Cast
    • Genre
    • Synopsis
    • Duration
    • IMDb rating
    • Release year
• Sort Options
  • Sort by year and genre.
• Wishlist
  • Save movies of interest.
  • Wishlist persists across sessions, even after logging out.
• Line Graphs
  • Displays rating fluctuations over specific periods: 1st day, 1st month, 3 months, 6 months, and 1 year.
• Creative Mode
  • Bar graphs show average IMDb ratings for:
    • Actor
    • Actress
    • Director
    • Genre
• Report Generation
  • Compare two movies on various metrics.
  • Generate top 5 movies by genre.
  • Generate top 5 movies by year (user-specified).

Description of Modules and Classes:

Detailed documentation of all modules and classes is provided in the codebase.

Work Done by Each Member:

Harshvardhan Mishra

Objective:

To manage the user interface (UI) design and implement automation for the IMDb project.

Key Features:

1. User Experience Enhancement:
   • Designed intuitive and visually appealing UI elements to improve user engagement.
   • Conducted user research and usability testing to identify pain points.
   • Implemented responsive design principles for seamless experiences across devices.
2. Workflow Streamlining:
   • Analyzed workflows for inefficiencies and bottlenecks.
   • Automated processes to reduce manual intervention.
   • Created documentation and training materials to support new workflows.
3. Seamless Integration:
   • Collaborated with cross-functional teams for smooth process integration.
   • Used APIs and tools to streamline data flow.

Value Added:

Resulted in a more efficient and user-friendly application, significantly enhancing user satisfaction.

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Divy Dobariya

Objective:

To automate backend processes and manage user data efficiently, ensuring seamless navigation.

Key Features:

1. Backend Automation and User Data Management:
   • Automated user data storage and retrieval.
   • Implemented secure, organized management systems using databases or file systems.
   • Enabled dynamic access and updates of user data.
2. Connecting Pages and Managing Navigation:
   • Ensured smooth transitions between application pages.
   • Implemented routing mechanisms for efficient navigation.
   • Enhanced user experience with intuitive workflows.

Value Added:

Resulted in an efficient, user-friendly application with improved operational efficiency.

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Tejas Kollipara

Objective:

Developed tools to analyze and visualize movie data interactively.

Key Features:

1. Bar Graphs:
   • Displayed average ratings by genre, actor, actress, and director.
2. Movie Comparison:
   • Enabled comparison of two movies on ratings, genre, and other metrics.
3. Top 5 Movies:
   • Listed top 5 movies by user-specified year or genre.
4. Rating Trends:
   • Used line graphs to track movie rating fluctuations over time.

Value Added:

• Simplified complex data visualization.
• Provided interactive tools for personalized analysis.
• Enhanced user insights into movie performance.

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G. Karthikeya

Objective:

To fetch and consolidate detailed movie data using TMDb and OMDb APIs.

Work Summary:

1. API Integration:
   • Connected to TMDb and OMDb APIs for movie details retrieval.
2. Data Collected:
   • Retrieved details such as Director, Plot, Synopsis, Genre, Ratings, Cast, Runtime, Budget, and Collection.
3. Data Validation:
   • Ensured accuracy and handled missing fields.
4. Storage:
   • Organized data into databases for easy querying and analysis.

Value Delivered:

• Centralized, accurate movie data storage.
• Enabled detailed exploration across various metrics.