Upload app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+from datasets import load_dataset
+from app.data_cleaning import prepare_document, cos_dicts, retrieve_top_k_similar
+from sklearn.feature_extraction.text import TfidfVectorizer
+import torch
+
+
+demo = gr.Blocks()
+
+df = load_dataset("arjunpatel/best-selling-video-games")
+df.set_format("pandas")
+df = df["train"][:]
+
+cleaned_wikis = df.wiki_page.apply(lambda x: prepare_document(x))
+tfidf = TfidfVectorizer()
+tfidf_wikis = tfidf.fit_transform(cleaned_wikis.tolist())
+video_game_cos_dict = cos_dicts(df.Title, tfidf_wikis.toarray())
+
+embedder = SentenceTransformer('msmarco-MiniLM-L6-cos-v5')
+msmarco_embeddings = embedder.encode(df.wiki_page.tolist(), convert_to_tensor = True)
+
+def nli_search(query):
+    # given a query, return top few similar games
+
+    # example code taken from Sentence Transformers docs
+    query_embedding = embedder.encode(query, convert_to_tensor=True)
+
+    # We use cosine-similarity and torch.topk to find the highest 5 scores
+    cos_scores = util.cos_sim(query_embedding, msmarco_embeddings)[0]
+    top_results = torch.topk(cos_scores, k=5)
+
+    #print("\n\n======================\n\n")
+    #print("Query:", query)
+    #print("\nTop 5 most similar sentences in corpus:")
+    ret_list = []
+
+    for score, idx in zip(top_results[0], top_results[1]):
+        ret_list.append((df.wiki_page.tolist()[idx][0:100], "(Score: {:.4f})".format(score)))
+    
+    return ret_list
+
+
+def find_similar_games(name, num):
+    return retrieve_top_k_similar(name, video_game_cos_dict, num)
+
+with demo:
+    gr.Markdown("<h1><center>Find your next Video Game!</center></h1>")
+    gr.Markdown(
+        """This Gradio demo allows you to search a list of best selling video games and their corresponding Wikipedia pages
+        using NLP! The first tab allows for a TF-IDF based search, and the second leverages Sentence Transformers for a Natural Language
+        Search. Enjoy!""")
+    with gr.Tab("TF-IDF Similarity Search"):
+        video_game = gr.Dropdown(df.Title.tolist(), default = df.Title.tolist()[0],
+        label = "Selected Game")
+
+        num_similar = gr.Dropdown([1, 2, 3, 4, 5], default = 1, label = "Number of Similar Games")
+
+        find_similar = gr.Button("Find 'em!")
+
+        output = gr.Textbox("Games will appear here!")
+
+        find_similar.click(fn = find_similar_games, inputs = [video_game, num_similar],
+        outputs = output)
+
+    with gr.Tab("Natural Language Search"):
+        q = gr.Textbox("Type a query here. Try: find me mario games")
+        find_nli = gr.Button("Search!")
+        nli_output = gr.Textbox("Output will appear here from NLI search")
+
+        find_nli.click(fn = nli_search, inputs = [q], outputs = nli_output)
+    
+
+
+demo.launch()
+#drop down for video game
+
+#drop down for number of similar games (1-5)
+
+#button to retrieve