Update app.py

app.py

@@ -5,10 +5,9 @@ from sklearn.metrics.pairwise import cosine_similarity
 import gradio as gr
 import numpy as np
 
-# Load the data - when deploying, adjust the path to where your dataset will be stored
+# Load the data
 def load_data():
     try:
-        # For Hugging Face Spaces deployment, you might need to adjust this path
         data = pd.read_csv('games_march2025_cleaned.csv', nrows=20000, on_bad_lines='skip', engine='python')
         return data
     except Exception as e:
@@ -91,57 +90,52 @@ def get_recommendations(game_name, data, feature_vectors):
 
     return result_html
 
-
 # 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."
+        return "Failed to load data."
+    
+    # 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."
+
     feature_vectors = prepare_features(data)
     recommendations_html = get_recommendations(game_name, data, feature_vectors)
     
     return recommendations_html
 
-    
-    # Format the output for Gradio
-    result_texts = []
-    result_images = []
-    
-    for result, image_url in recommendations:
-        result_texts.append(result)
-        if image_url and str(image_url) != 'nan':
-            result_images.append(image_url)
-        else:
-            # Use a placeholder image if no image URL is available
-            result_images.append(None)
-    
-    # Create a gallery of results
-    results_html = ""
-    for i, (text, img) in enumerate(zip(result_texts, result_images)):
-        results_html += text
-    
-    return results_html, result_images
-
 # 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="Input Steam Game")
     
     with gr.Row():
-        input_text = gr.Textbox(label="Enter your favorite game:")
+        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()