Added Chart Display

app.py

@@ -1,5 +1,6 @@
 import pandas as pd
 import gradio as gr
+import plotly.express as px
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 from sklearn.preprocessing import MinMaxScaler
@@ -9,43 +10,35 @@ import difflib
 data = pd.read_csv('steam.csv', quotechar='"', on_bad_lines='skip', nrows=10000)
 data.fillna('', inplace=True)
 
-# Combine better features
+# Combine features
 selected_features = ['genres', 'categories', 'tags']
 for feature in selected_features:
     if feature not in data.columns:
         data[feature] = ''
-
 data['combined_features'] = data['genres'] + ' ' + data['categories'] + ' ' + data['tags']
 
-# TF-IDF Vectorizer tuned
-vectorizer = TfidfVectorizer(
-    stop_words='english', 
-    ngram_range=(1, 2),
-    max_features=8000
-)
+# Vectorize
+vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1, 2), max_features=8000)
 feature_vectors = vectorizer.fit_transform(data['combined_features'])
 
-# Normalize positive ratings for re-ranking
+# Normalize positive ratings
 scaler = MinMaxScaler()
 data['positive_ratings_scaled'] = scaler.fit_transform(data[['positive_ratings']])
 
-# Calculate cosine similarity
+# Similarity matrix
 game_similarity = cosine_similarity(feature_vectors)
-
-# List of titles
 list_of_all_titles = data['name'].tolist()
 
+# Recommend function
 def recommend_games(user_game_name_input):
     find_close_match = difflib.get_close_matches(user_game_name_input, list_of_all_titles, n=1)
     if not find_close_match:
-        return "No close match found. Please try another game name."
-    
+        return "No close match found. Please try another game name.", None
+
     closest_match = find_close_match[0]
     index_of_the_game = data.loc[data['name'] == closest_match].index[0]
 
     similarity_scores = list(enumerate(game_similarity[index_of_the_game]))
-    
-    # Sort by similarity and positive ratings
     sorted_similar_games = sorted(
         similarity_scores, 
         key=lambda x: (x[1], data.iloc[x[0]]['positive_ratings_scaled']), 
@@ -53,22 +46,24 @@ def recommend_games(user_game_name_input):
     )
 
     recommendations = []
-    for i, (index, score) in enumerate(sorted_similar_games[1:21]):  # Skip itself
+    chart_data = []
+    for i, (index, score) in enumerate(sorted_similar_games[1:21]):
         if score < 0.3:
-            continue  # Skip very low similarity
+            continue
         game_name = data.iloc[index]['name']
         recommendations.append(f"{i+1}. {game_name} (Similarity: {score:.2f})")
+        chart_data.append({'Game': game_name, 'Similarity': score})
         if len(recommendations) >= 10:
             break
 
-    return "\n".join(recommendations)
+    chart_df = pd.DataFrame(chart_data)
+    return "\n".join(recommendations), chart_df
 
-# Simulate evaluation Precision@5
+# Precision@5
 def evaluate_precision(user_game_name_input):
     find_close_match = difflib.get_close_matches(user_game_name_input, list_of_all_titles, n=1)
     if not find_close_match:
         return 0.0
-    
     closest_match = find_close_match[0]
     index_of_the_game = data.loc[data['name'] == closest_match].index[0]
     similarity_scores = list(enumerate(game_similarity[index_of_the_game]))
@@ -77,25 +72,37 @@ def evaluate_precision(user_game_name_input):
         key=lambda x: (x[1], data.iloc[x[0]]['positive_ratings_scaled']), 
         reverse=True
     )
-    
     top_5 = [data.iloc[idx]['genres'] for idx, _ in sorted_similar_games[1:6]]
     original_genre = data.iloc[index_of_the_game]['genres']
     hits = sum(1 for genre in top_5 if genre == original_genre)
-    precision_at_5 = hits / 5
-    return round(precision_at_5, 2)
+    return round(hits / 5, 2)
 
-# Gradio App
-def recommend_and_score(user_input):
-    recommendations = recommend_games(user_input)
+# Combined Gradio function
+def recommend_and_visualize(user_input):
+    recommendations, chart_df = recommend_games(user_input)
     precision = evaluate_precision(user_input)
-    return f"{recommendations}\n\nPrecision@5 (approx): {precision}"
-
-demo = gr.Interface(
-    fn=recommend_and_score,
-    inputs=gr.Textbox(lines=1, placeholder="Enter your favorite game"),
-    outputs="text",
-    title="Steam Video Game Recommender",
-    description="Enter a game name and get some recommendations!"
-)
+    chart = None
+
+    if chart_df is not None and not chart_df.empty:
+        chart = px.bar(chart_df, x="Game", y="Similarity", title="Top Game Recommendations", 
+                       labels={"Similarity": "Cosine Similarity Score"}, height=400)
+    
+    return recommendations + f"\n\nPrecision@5 (approx): {precision}", chart
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎮 Steam Game Recommender")
+    gr.Markdown("Enter the name of a game you like and get recommendations based on similarity!")
+
+    with gr.Row():
+        input_box = gr.Textbox(label="Your Favorite Game", placeholder="e.g., Portal 2")
+
+    with gr.Row():
+        output_text = gr.Textbox(label="Recommendations", lines=12, interactive=False)
+        output_chart = gr.Plot(label="Recommendation Chart")
+
+    run_button = gr.Button("Recommend")
+
+    run_button.click(fn=recommend_and_visualize, inputs=input_box, outputs=[output_text, output_chart])
 
 demo.launch()