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app.py

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+# Importing necessary libraries from Flask
+from flask import Flask, render_template, request  # Flask creates web applications; render_template serves HTML, and request handles form data.
+import pickle  # pickle is used for loading pre-trained models or data stored in serialized files.
+import requests  # requests is used to make HTTP requests to external APIs.
+
+# Initializing a Flask application
+app = Flask(__name__)
+
+# Load the movie list and similarity matrix
+movies = pickle.load(open('artifacts/model.pkl', 'rb'))  # Loads the list of movies from a serialized file.
+similarity = pickle.load(open('artifacts/similarity.pkl', 'rb'))  # Loads the similarity matrix for movies, used for recommendations.
+
+# Function to fetch the poster image of a movie from The Movie Database (TMDB) API
+def fetch_poster(movie_id):
+    # TMDB API URL, dynamically formatted with a movie ID and API key to get movie details
+    url = f"https://api.themoviedb.org/3/movie/{movie_id}?api_key=8265bd1679663a7ea12ac168da84d2e8&language=en-US"
+    data = requests.get(url).json()  # Sends a GET request to the URL and retrieves the response in JSON format.
+    poster_path = data.get('poster_path')  # Extracts the poster path if available.
+    if poster_path:  # Checks if a poster path exists for the movie.
+        return "https://image.tmdb.org/t/p/w500/" + poster_path  # Returns the full URL for the poster image.
+    return None  # If no poster is available, returns None.
+
+# Function to recommend similar movies
+def recommend(movie):
+    # Gets the index of the selected movie in the movies DataFrame based on the movie title
+    index = movies[movies['title'] == movie].index[0]
+    # Enumerates similarity scores with the selected movie and sorts them in descending order
+    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])
+    recommended_movie_names = []  # List to store recommended movie titles.
+    recommended_movie_posters = []  # List to store corresponding poster images.
+
+    # Loop to get the top 5 similar movies (ignoring the first entry, which is the movie itself)
+    for i in distances[1:5]:
+        movie_id = movies.iloc[i[0]].movie_id  # Gets the movie ID for each recommended movie.
+        recommended_movie_posters.append(fetch_poster(movie_id))  # Fetches and appends the movie's poster.
+        recommended_movie_names.append(movies.iloc[i[0]].title)  # Adds the movie title to recommendations.
+
+    return recommended_movie_names, recommended_movie_posters  # Returns lists of recommended movie titles and posters.
+
+# Route for the homepage, handling both GET and POST requests
+@app.route('/', methods=['GET', 'POST'])
+def index():
+    # When a movie is selected and form is submitted (POST request)
+    if request.method == 'POST':
+        selected_movie = request.form.get('movie')  # Retrieves the selected movie from the form.
+        # Calls the recommend function and gets recommended movie names and posters
+        recommended_movie_names, recommended_movie_posters = recommend(selected_movie)
+        # Renders the HTML page with the selected movie, recommendations, and movie list for dropdown
+        return render_template('index.html', selected_movie=selected_movie,
+                               recommended_movie_names=recommended_movie_names,
+                               recommended_movie_posters=recommended_movie_posters,
+                               movies=movies['title'].values,
+                               zip=zip)  # Adds zip function to enable pairing movie names with posters.
+
+    # For GET requests, render the page with the movie list only.
+    return render_template('index.html', movies=movies['title'].values)
+
+# Running the application in debug mode for development (provides error details on crashes)
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+numpy
+pandas
+matplotlib
+seaborn
+scikit-learn
+ipykernel
+dill
+flask
+flask_cors
+lime
+gunicorn
+nltk
+#-e .

setup.py

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+from setuptools import find_packages, setup
+from typing import List
+
+HYPEN_E_DOT = '-e .'
+
+def get_requirements(file_path: str) -> List[str]:
+    '''
+    This function will return the list of requirements from the specified file.
+    '''
+    requirements = []
+    with open(file_path) as file_obj:
+        requirements = file_obj.readlines()
+        # Strip whitespace and newlines from each requirement
+        requirements = [req.strip() for req in requirements]
+
+        # Remove the editable install if present
+        if HYPEN_E_DOT in requirements:
+            requirements.remove(HYPEN_E_DOT)
+
+    return requirements
+
+# Setup function to define package metadata and dependencies
+setup(
+    name="Movie_Recommender_System_Content_Based_Filtering",  # Updated project name
+    version="1.0.0",
+    packages=find_packages(),
+    # Package metadata
+    author="Suresh Beekhani",
+    author_email="sureshbeekhani26@gmail.com",
+    description="A tool for recommending movies based on content-based filtering techniques.",
+    long_description="This package provides a comprehensive model for recommending movies using content-based filtering and data analysis techniques.",
+    long_description_content_type="text/markdown",
+    url="https://github.com/SURESHBEEKHANI/Movie-Recommender-System-Content-Based-Filtering.git",  # Updated URL to match your project
+    # Read dependencies from requirements.txt
+    install_requires=get_requirements('requirements.txt'),
+    # License and classification
+    license="MIT",
+    classifiers=[
+        "Programming Language :: Python :: 3.12",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+    ],
+)