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app.py

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+from flask import Flask, request, render_template, jsonify
+import pickle
+import pandas as pd
+from sklearn.neighbors import NearestNeighbors
+import numpy as np
+
+app = Flask(__name__)
+
+# ---------------------------
+# Load saved model components
+# ---------------------------
+with open("train_df.pkl", "rb") as f:
+    train_df = pickle.load(f)
+with open("mlb.pkl", "rb") as f:
+    mlb = pickle.load(f)
+with open("train_genre_features.pkl", "rb") as f:
+    train_genre_features = pickle.load(f)
+# (Optional) Loading nn_model if needed:
+# with open("nn_model.pkl", "rb") as f:
+#     nn_model = pickle.load(f)
+
+# If train_df does not have a list of genres, create it from the 'genres' column
+if "genre_list" not in train_df.columns:
+    train_df["genre_list"] = train_df["genres"].apply(lambda x: x.split("|"))
+
+# Prepare a list of all genres (for the dropdown options)
+all_genres = sorted({genre for sublist in train_df["genre_list"] for genre in sublist})
+
+# Prepare rating options (0 to 5 in increments of 0.5)
+rating_options = [str(i / 2) for i in range(0, 11)]
+# Prepare recommendation number options (1 to 10)
+recommendation_options = [str(i) for i in range(1, 11)]
+
+
+# ---------------------------
+# Recommendation function using the training model
+# ---------------------------
+def recommend_movies_train(input_genres, min_rating, max_rating, n_recommendations=5, use_filter=True):
+    """
+    Recommend movies using the training set model.
+
+    Parameters:
+        input_genres (str): Comma-separated string of genres (e.g., "Comedy, Drama")
+        min_rating (float): Minimum average rating.
+        max_rating (float): Maximum average rating.
+        n_recommendations (int): Number of recommendations.
+        use_filter (bool): Whether to filter the training set by genre string matching.
+
+    Returns:
+        pd.DataFrame: Recommended movies from the training set.
+    """
+    # Clean and process the input genres: replace any "|" with commas and split
+    cleaned_input = input_genres.replace("|", ",")
+    input_genre_list = [g.strip() for g in cleaned_input.split(',') if g.strip()]
+
+    if not input_genre_list:
+        return pd.DataFrame()
+
+    # Filter training movies by the given rating range
+    filtered_train = train_df[(train_df['avg_rating'] >= min_rating) & (train_df['avg_rating'] <= max_rating)]
+
+    # Optionally filter training movies to keep those that have one of the input genres
+    if use_filter:
+        genre_pattern = '|'.join(input_genre_list)
+        filtered_train = filtered_train[filtered_train['genres'].str.contains(genre_pattern, case=False, na=False)]
+
+    if filtered_train.empty:
+        return pd.DataFrame()
+
+    # Get indices of the filtered training data relative to the full training set
+    filtered_indices = filtered_train.index.to_numpy()
+
+    # Obtain the corresponding genre features from the training set features
+    filtered_features = train_genre_features[[list(train_df.index).index(i) for i in filtered_indices]]
+
+    # Create the input vector using the same MultiLabelBinarizer
+    input_vector = mlb.transform([input_genre_list])
+
+    # Fit a temporary nearest neighbors model on the filtered training data
+    nn_filtered = NearestNeighbors(metric='cosine')
+    nn_filtered.fit(filtered_features)
+
+    n_neighbors = min(n_recommendations, len(filtered_train))
+    distances, indices = nn_filtered.kneighbors(input_vector, n_neighbors=n_neighbors)
+
+    # Map relative indices back to the original training DataFrame indices
+    recommended_indices = filtered_indices[indices[0]]
+
+    return train_df.loc[recommended_indices][['movieId', 'title', 'avg_rating', 'genres']]
+
+
+# ---------------------------
+# Routes
+# ---------------------------
+@app.route("/", methods=["GET", "POST"])
+def index():
+    if request.method == "POST":
+        # Get form inputs from dropdowns
+        selected_genres = request.form.getlist("genres")
+        # Join selected genres into a comma-separated string
+        input_genres = ", ".join(selected_genres)
+
+        try:
+            min_rating = float(request.form.get("min_rating", 0))
+            max_rating = float(request.form.get("max_rating", 5))
+            n_recommendations = int(request.form.get("n_recommendations", 5))
+        except ValueError:
+            return render_template("index.html", error="Invalid rating or recommendation number.",
+                                   all_genres=all_genres, rating_options=rating_options,
+                                   recommendation_options=recommendation_options)
+
+        if min_rating > max_rating:
+            return render_template("index.html", error="Minimum rating cannot be greater than maximum rating.",
+                                   all_genres=all_genres, rating_options=rating_options,
+                                   recommendation_options=recommendation_options)
+
+        # Get recommendations from the model
+        recommendations = recommend_movies_train(input_genres, min_rating, max_rating, n_recommendations)
+
+        if recommendations.empty:
+            message = "No movies found for the given criteria."
+            return render_template("index.html", message=message,
+                                   all_genres=all_genres, rating_options=rating_options,
+                                   recommendation_options=recommendation_options)
+        else:
+            # Convert DataFrame to HTML table for display
+            rec_html = recommendations.to_html(classes="table table-striped", index=False)
+            return render_template("index.html", recommendations=rec_html,
+                                   all_genres=all_genres, rating_options=rating_options,
+                                   recommendation_options=recommendation_options)
+
+    # GET request: pass dropdown options to template
+    return render_template("index.html", all_genres=all_genres, rating_options=rating_options,
+                           recommendation_options=recommendation_options)
+
+
+@app.route("/api/recommend", methods=["POST"])
+def api_recommend():
+    data = request.get_json()
+    input_genres = data.get("genres", "")
+    try:
+        min_rating = float(data.get("min_rating", 0))
+        max_rating = float(data.get("max_rating", 5))
+        n_recommendations = int(data.get("n_recommendations", 5))
+    except ValueError:
+        return jsonify({"error": "Invalid rating or recommendation number."}), 400
+
+    recommendations = recommend_movies_train(input_genres, min_rating, max_rating, n_recommendations)
+    if recommendations.empty:
+        return jsonify({"message": "No movies found for the given criteria."})
+
+    result = recommendations.to_dict(orient="records")
+    return jsonify(result)
+
+
+if __name__ == "__main__":
+    app.run(debug=True)

mlb.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:128c83e1bf2c6cb5555f256109c1ecbdd84e3acc31c208c0aaea2e8a9a2aa79c
+size 585

nn_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9d9eb8d14a4ef1c7492505c2b347537ce3d350700d79270bb36e5257a98ecc4
+size 7558421

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>CineSage - Movie Recommendation System</title>
+    
+    <!-- Bootstrap 5 CSS -->
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet">
+    
+    <!-- Google Fonts -->
+    <link href="https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600&display=swap" rel="stylesheet">
+    
+    <style>
+        body {
+            background-color: #f4f6f9;
+            font-family: 'Poppins', sans-serif;
+        }
+        .recommendation-container {
+            background-color: white;
+            border-radius: 12px;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+            padding: 30px;
+            margin-top: 20px;
+        }
+        .form-control {
+            border-radius: 8px;
+            transition: all 0.3s ease;
+        }
+        .form-control:focus {
+            box-shadow: 0 0 0 0.2rem rgba(13, 110, 253, 0.25);
+            border-color: #0d6efd;
+        }
+        .btn-primary {
+            background-color: #0d6efd;
+            border: none;
+            border-radius: 8px;
+            transition: all 0.3s ease;
+        }
+        .btn-primary:hover {
+            background-color: #0b5ed7;
+            transform: translateY(-2px);
+        }
+        .movie-recommendations {
+            display: grid;
+            grid-template-columns: repeat(auto-fill, minmax(250px, 1fr));
+            gap: 20px;
+        }
+        .movie-card {
+            background-color: #f8f9fa;
+            border-radius: 12px;
+            padding: 15px;
+            transition: transform 0.3s ease;
+        }
+        .movie-card:hover {
+            transform: scale(1.03);
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+        }
+        .rating-badge {
+            background-color: #28a745;
+            color: white;
+            padding: 5px 10px;
+            border-radius: 20px;
+        }
+    </style>
+</head>
+<body>
+    <div class="container py-5">
+        <div class="row justify-content-center">
+            <div class="col-lg-8">
+                <div class="recommendation-container">
+                    <h1 class="text-center mb-4">CineSage 🎬 Movie Recommender</h1>
+                    
+                    <!-- Error and Message Alerts -->
+                    {% if error %}
+                    <div class="alert alert-danger alert-dismissible fade show" role="alert">
+                        {{ error }}
+                        <button type="button" class="btn-close" data-bs-dismiss="alert" aria-label="Close"></button>
+                    </div>
+                    {% endif %}
+                    
+                    {% if message %}
+                    <div class="alert alert-info alert-dismissible fade show" role="alert">
+                        {{ message }}
+                        <button type="button" class="btn-close" data-bs-dismiss="alert" aria-label="Close"></button>
+                    </div>
+                    {% endif %}
+                    
+                    <!-- Recommendation Form -->
+                    <form method="POST" action="/">
+                        <div class="row g-3">
+                            <div class="col-md-6">
+                                <label for="genres" class="form-label">Movie Genres</label>
+                                <input type="text" class="form-control" name="genres" id="genres" 
+                                       placeholder="Comedy, Drama, Sci-Fi" required>
+                                <small class="form-text text-muted">Separate genres with commas</small>
+                            </div>
+                            
+                            <div class="col-md-3">
+                                <label for="min_rating" class="form-label">Min Rating</label>
+                                <input type="number" class="form-control" name="min_rating" id="min_rating" 
+                                       min="0" max="5" step="0.5" required>
+                            </div>
+                            
+                            <div class="col-md-3">
+                                <label for="max_rating" class="form-label">Max Rating</label>
+                                <input type="number" class="form-control" name="max_rating" id="max_rating" 
+                                       min="0" max="5" step="0.5" required>
+                            </div>
+                            
+                            <div class="col-12">
+                                <label for="n_recommendations" class="form-label">Number of Recommendations</label>
+                                <input type="number" class="form-control" name="n_recommendations" 
+                                       id="n_recommendations" min="1" max="20" value="5" required>
+                            </div>
+                            
+                            <div class="col-12">
+                                <button type="submit" class="btn btn-primary w-100 mt-3">
+                                    Discover Movies 🔍
+                                </button>
+                            </div>
+                        </div>
+                    </form>
+                </div>
+                
+                <!-- Recommendations Section -->
+                {% if recommendations %}
+                <div class="mt-5">
+                    <h2 class="text-center mb-4">Your Movie Recommendations</h2>
+                    <div class="movie-recommendations">
+                        {{ recommendations|safe }}
+                    </div>
+                </div>
+                {% endif %}
+            </div>
+        </div>
+    </div>
+
+    <!-- Bootstrap 5 JS and Popper.js -->
+    <script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.6/dist/umd/popper.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.min.js"></script>
+</body>
+</html>

train_df.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:706077a31bac5b3d7da5eacf1f3c527ac3bf203dea5facc59bac57bde4b96d60
+size 3629725

train_genre_features.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:08cfe0ced4914ac06c73fca723136d1c3f3ec4da94babe1d3bb7fd0b40ccad2a
+size 7558083