Adding Filters

app.py

@@ -17,7 +17,7 @@ def load_data():
 
 # Prepare the feature vectors for similarity calculation
 def prepare_features(data):
-    selected_features = ['genres', 'price', 'average_playtime_2weeks', 'tags', 'average_playtime_forever']
+    selected_features = ['genres', 'price', 'average_playtime_2weeks', 'tags']
     
     for feature in selected_features:
         data[feature] = data[feature].fillna('')
@@ -26,8 +26,7 @@ def prepare_features(data):
         data['genres'] + ' ' +
         data['price'].astype(str) + ' ' +
         data['average_playtime_2weeks'].astype(str) + ' ' +
-        data['tags'].astype(str) + ' ' +
-        data['average_playtime_forever'].astype(str)
+        data['tags'].astype(str)
     )
     
     vectorizer = TfidfVectorizer()
@@ -94,16 +93,25 @@ def get_recommendations(game_name, data, feature_vectors):
 
 
 # Gradio interface function
-def recommend_games(game_name):
+def recommend_games(game_name, max_age, max_price, min_metacritic):
     data = load_data()
     if data is None:
         return "Failed to load data. Please check the data file."
     
+    # Apply filters BEFORE feature preparation
+    data = data[
+        (data['required_age'] <= max_age) &
+        (data['price'] <= max_price) &
+        ((data['metacritic_score'].fillna(0) >= min_metacritic) | data['metacritic_score'].isna())
+    ].reset_index(drop=True)
+    
+    if data.empty:
+        return "No games found matching your filter criteria."
+
     feature_vectors = prepare_features(data)
     recommendations_html = get_recommendations(game_name, data, feature_vectors)
     
     return recommendations_html
-
     
     # Format the output for Gradio
     result_texts = []
@@ -126,23 +134,31 @@ def recommend_games(game_name):
 
 # Create the Gradio interface
 with gr.Blocks(title="Steam Game Recommender") as demo:
-    gr.Markdown("# Steam Game Recommender")
-    gr.Markdown("Enter your favorite game to get recommendations for similar games.")
-    
+    gr.Markdown("# 🎮 Steam Game Recommender")
+    gr.Markdown("Enter a game you like and customize filters to get similar suggestions.")
+
     with gr.Row():
-        input_text = gr.Textbox(label="Enter your favorite game:")
-        submit_btn = gr.Button("Get Recommendations")
+        input_text = gr.Textbox(label="🎯 Favorite Game")
     
     with gr.Row():
-        output_text = gr.Markdown(label="Recommendations")
-    
+        max_age_slider = gr.Slider(0, 21, value=17, label="Max Age Rating (Avoid Adult Games)")
+        max_price_slider = gr.Slider(0.0, 100.0, value=60.0, step=0.5, label="Maximum Price ($)")
+        min_metacritic_slider = gr.Slider(0, 100, value=50, step=1, label="Minimum Metacritic Score")
+
+    with gr.Row():
+        submit_btn = gr.Button("🔍 Get Recommendations")
+
+    with gr.Row():
+        output_text = gr.Markdown(label="🧠 Recommendations")
+
     submit_btn.click(
         fn=recommend_games,
-        inputs=input_text,
+        inputs=[input_text, max_age_slider, max_price_slider, min_metacritic_slider],
         outputs=output_text
     )
 
 
+
 # Launch the app
 if __name__ == "__main__":
     demo.launch()