Update app.py

app.py

@@ -35,60 +35,69 @@ def prepare_features(data):
 
 # Function to get game recommendations
 def get_recommendations(game_name, data, feature_vectors):
-    list_of_all_titles = data['name'].tolist()
-    find_close_match = difflib.get_close_matches(game_name, list_of_all_titles)
-    
-    if not find_close_match:
-        return "No match found for the game name. Please try another title."
-    
-    closest_match = find_close_match[0]
-    index_of_the_game = data.loc[data['name'] == closest_match].index[0]
-    
-    game_similarity = cosine_similarity(feature_vectors)
-    similarity_scores = list(enumerate(game_similarity[index_of_the_game]))
-    sorted_similar_games = sorted(similarity_scores, key=lambda x: x[1], reverse=True)
+    titles = data['name'].tolist()
+    close_matches = difflib.get_close_matches(game_name, titles)
+
+    if not close_matches:
+        print(f"Couldn't find a close match for: '{game_name}'")
+        return "No match found. Try typing a more complete or accurate title."
 
-    result_html = ""
+    match = close_matches[0]
+    print(f"Using closest match: {match}")
+    game_idx = data[data['name'] == match].index[0]
 
-    for i, game in enumerate(sorted_similar_games[1:10], 1):
-        index = game[0]
-        name = data.loc[index, 'name']
-        about = data.loc[index, 'short_description'] or "No description available"
-        image_url = data.loc[index, 'header_image'] or ""
+    similarity = cosine_similarity(feature_vectors)
+    scores = list(enumerate(similarity[game_idx]))
+    similar_games = sorted(scores, key=lambda x: x[1], reverse=True)
 
+    html = ""
+
+    for i, (idx, score) in enumerate(similar_games[1:10], 1):  # skip the first one (it's the same game)
+        game = data.loc[idx]
+        name = game['name']
+        desc = game['short_description'] or "No description provided."
+        if len(desc) > 180:
+            desc = desc[:180] + "..."
+
+        img = game.get('header_image', '') or ""
+
+        # Detect supported platforms
         platforms = []
-        if data.loc[index, 'windows'] == 1:
-            platforms.append("Windows")
-        if data.loc[index, 'mac'] == 1:
-            platforms.append("Mac")
-        if data.loc[index, 'linux'] == 1:
-            platforms.append("Linux")
-        platforms_str = ", ".join(platforms) or "Unknown"
-
-        metacritic = data.loc[index, 'metacritic_score']
-        price = data.loc[index, 'price']
-        pos = data.loc[index, 'positive']
-        neg = data.loc[index, 'negative']
-        total_reviews = pos + neg
-        pos_ratio = f"{(pos / total_reviews * 100):.1f}%" if total_reviews > 0 else "N/A"
-
-        # Combine into HTML
-        result_html += f"""
-        <div style="display:flex; align-items:flex-start; margin-bottom:20px;">
-            <img src="{image_url}" style="width:150px; height:auto; margin-right:15px; border-radius:8px;">
+        if game.get('windows') == 1:
+            platforms.append('Windows')
+        if game.get('mac') == 1:
+            platforms.append('Mac')
+        if game.get('linux') == 1:
+            platforms.append('Linux')
+        platforms_str = ", ".join(platforms) if platforms else "Unknown"
+
+        price = game['price']
+        meta_score = game.get('metacritic_score')
+        meta_display = int(meta_score) if pd.notnull(meta_score) else "N/A"
+
+        pos = game.get('positive', 0)
+        neg = game.get('negative', 0)
+        total = pos + neg
+        pos_pct = f"{(pos / total * 100):.1f}%" if total > 0 else "N/A"
+
+        # Build the card HTML
+        html += f'''
+        <div style="display:flex; margin-bottom:16px;">
+            <img src="{img}" width="130" style="margin-right:12px; border-radius:4px;">
             <div>
-                <h3>{i}. {name}</h3>
+                <h4>{i}. {name}</h4>
                 <p><b>Platforms:</b> {platforms_str}</p>
                 <p><b>Price:</b> ${price}</p>
-                <p><b>Metacritic Score:</b> {metacritic if pd.notnull(metacritic) else "N/A"}</p>
-                <p><b>Positive Reviews:</b> {pos_ratio}</p>
-                <p>{about}</p>
+                <p><b>Metacritic:</b> {meta_display}</p>
+                <p><b>Positive Reviews:</b> {pos_pct}</p>
+                <p>{desc}</p>
             </div>
         </div>
         <hr>
-        """
+        '''
+
+    return html
 
-    return result_html
 
 # Gradio interface function
 def recommend_games(game_name, max_age, max_price, min_metacritic):