Pull images

backend/app.py

@@ -4,6 +4,12 @@
 import pandas as pd
 import html
 
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+
+import requests
+from bs4 import BeautifulSoup
 app = Flask(__name__)
 CORS(app)
 
@@ -12,7 +18,13 @@
 data_df = pd.read_csv(csv_file_path)
 
 
-def csv_search(query, min_age, min_players, max_players, category):
+# for cosine sim
+data_df['processed_description'] = data_df['description'].apply(lambda x: x.lower() if isinstance(x, str) else '')
+tfidf_vectorizer = TfidfVectorizer()
+tfidf_matrix = tfidf_vectorizer.fit_transform(data_df['processed_description'].values.astype('U'))  # 'U' for Unicode
+
+
+def basic_search(query, min_age, min_players, max_players, category):
     matches = data_df[data_df['name'].str.lower().str.contains(query.lower())]
     if min_age is not None:
         matches = matches[matches['minage'] >= min_age]
@@ -34,23 +46,97 @@ def home():
     return render_template('base.html', title="sample html")
 
 @app.route("/games")
-def episodes_search():
+def search():
     text = request.args.get("title")
     min_age = request.args.get("min_age", type=int)
     min_players = request.args.get("min_players", type=int)
     max_players = request.args.get("max_players", type=int)
     category = request.args.get("category")
-    return csv_search(text, min_age, min_players, max_players, category)
+    mode = request.args.get("mode")  # Added to capture the search mode
+
+    if mode == 'recommendation':
+        matches = recommendation_search(text)
+    else:
+        matches = matches = data_df[data_df['name'].str.lower().str.contains(text.lower())]
+        # basic_search(text, min_age, min_players, max_players, category)
+
+    if min_age is not None:
+        matches = matches[matches['minage'] >= min_age]
+    if min_players is not None:
+        matches = matches[matches['minplayers'] >= min_players]
+    if max_players is not None:
+        matches = matches[matches['maxplayers'] <= max_players]
+    if category:
+        matches = matches[matches['boardgamecategory'].str.contains(category, case=False, na=False)]
+    matches_filtered = matches[['name', 'description', 'average', 'objectid']] 
+    matches_filtered['name'] = matches_filtered['name'].apply(html.unescape)
+    matches_filtered['description'] = matches_filtered['description'].apply(html.unescape)
+    matches_filtered_json = matches_filtered.to_json(orient='records')
+
+    # Return only the relevant columns
+    return matches_filtered_json
+
+def recommendation_search(query):
+    if query:
+        #some preprocessing: 1- fetch the game 2- get its processed desc
+        game_row = data_df[data_df['name'].str.lower() == query.lower()].iloc[0]
+        game_description = game_row['processed_description']
+
+        #cos sim
+        query_vector = tfidf_vectorizer.transform([game_description])
+        cosine_similarities = cosine_similarity(query_vector, tfidf_matrix).flatten()
+
+        #get the top 1000       
+        similar_indices = cosine_similarities.argsort()[-(1000+1):][::-1]
+    game_index = game_row.index[0]
+
+    # It's possible the game itself is the most similar one, so we exclude it if so.
+    similar_indices = similar_indices[1:]
+
+    # Fetch the details of the top N similar games then return
+    similar_games = data_df.iloc[similar_indices]
+    return similar_games
+
 
 @app.route("/about/<game_id>")
 def about(game_id):
     game_id = int(game_id)
     game_details_query = data_df[data_df['objectid'] == game_id]
     if not game_details_query.empty:
         game_details = game_details_query.iloc[0].to_dict()
+        print("!!!! LOOK HERE !!!!")
+        print(game_details)
+        # game_img = fetch_game_link(game_details_query["gamelink"])
+        print(game_id)
+        game_img = fetch_game_link(game_details_query.reset_index(drop=True).at[0, "gamelink"])
+        print("!!!! LOOK HERE !!!!")
+        print(game_img)
+        game_details["img"] = game_img
         return render_template('about.html', game=game_details)
     else:
         return "Game not found", 404
+
+def fetch_game_link(game_link):
+    # game_link = game_link[game_link.index("/"):]
+    print("!!!! LOOK HERE !!!!")
+    print(game_link)
+    game_url = f"https://boardgamegeek.com{game_link}"
+    print("!!!! LOOK HERE !!!!")
+    print(game_link)
+    response = requests.get(game_url)
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.text, 'html.parser')
+        img_tag = soup.find('meta', attrs={"property": "og:image"})
 
+        if img_tag:
+            return img_tag['content']
+            # if img_tag and 'src' in img_tag.attrs:
+            #     return img_tag['src']
+            # else:
+            #     return "No surrounding <a> tag with an href found."
+        else:
+            return "No <img> tag with the specified class found."
+    else:
+        return f"Failed to retrieve the webpage. Status code: {response.status_code}"
 if __name__ == '__main__':
     app.run(debug=True, host="0.0.0.0", port=5000)