Added examples

app.py

@@ -10,77 +10,86 @@ from gensim.models.fasttext import load_facebook_model
 
 ACCESS_KEY = os.environ.get('ACCESS_KEY')
 
-# download model from huggingface hub
+
+# Setup model
 url = hf_hub_url(repo_id="simonschoe/call2vec", filename="model.bin")
 cached_download(url)
-
-# load model via gensim
 model = load_facebook_model(cached_download(url))
 
-def process(_input, topn):
+def semantic_search(_input, n):
+    """ Perform semantic search """
 
-    # split by delimiting characters
     _input = re.split('[,;\n]', _input)
-
-    # convert input to lower, replace whitespaces by underscores
     _input = [s.strip().lower().replace(' ', '_') for s in _input if s]
 
     if _input[0] != ACCESS_KEY:
         with open('log.txt', 'a') as f:
             f.write(str(datetime.now()) + '+++' + '___'.join(_input) + '\n')
 
-    # apply model
-    if len(_input)>1:
-        # compute average seed embedding
+    if len(_input) > 1:
         avg_input = np.stack([model.wv[w] for w in _input], axis=0).mean(axis=0)
-        # find similarities
-        nearest_neighbors = model.wv.most_similar(positive=avg_input, topn=topn)
-        frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbors]
+        nearest_neighbours = model.wv.most_similar(positive=avg_input, topn=n)
+        frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbours]
     else:
-        # find similarities
-        nearest_neighbors = model.wv.most_similar(positive=_input[0], topn=topn)
-        frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbors]
-
-    result = pd.DataFrame([(a[0],a[1],b) for a,b in zip(nearest_neighbors, frequencies)], columns=['Token', 'Cosine Similarity', 'Frequency'])
+        nearest_neighbours = model.wv.most_similar(positive=_input[0], topn=n)
+        frequencies = [model.wv.get_vecattr(nn[0], 'count') for nn in nearest_neighbours]
+    
     if _input[0] == ACCESS_KEY:
         with open('log.txt', 'r') as f:
             prompts = f.readlines()
             prompts = [p.strip().split('+++') for p in prompts]
-        result = pd.DataFrame(prompts, columns=['Time', 'Prompt'])
+        result = pd.DataFrame(prompts,
+                              columns=['Time', 'Prompt'])
+    else:
+        result = pd.DataFrame([(a[0],a[1],b) for a,b in zip(nearest_neighbours, frequencies)],
+                              columns=['Token', 'Cosine Similarity', 'Corpus Frequency'])
+    
     result.to_csv('result.csv')
     return result, 'result.csv', '\n'.join(_input)
 
-def save(df):
-    df.to_csv('result.csv')
-    return 'result.csv'
-
-demo = gr.Blocks()
+app = gr.Blocks()
 
-with demo:
+with app:
     gr.Markdown("# Call2Vec")
-    gr.Markdown("## Earnings call transformation project")
+    gr.Markdown("## Semantic Search in Quarterly Earnings Conference Calls")
     with gr.Row():
         with gr.Column():
-            gr.Markdown("""
+            gr.Markdown(
+                """
                 #### Project Description
-                Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet.""")
+                Call2Vec is a [fastText](https://fasttext.cc/) word embedding model trained via [Gensim](https://radimrehurek.com/gensim/). It maps each token in the vocabulary into a dense, 300-dimensional vector space, designed for performing semantic search.
+                The model is trained on a large sample of quarterly earnings conference calls, held by U.S. firms during the 2006-2022 period. In particular, the training data is restriced to the (rather sponentous) executives' remarks of the Q&A section of the call. The data has been preprocessed prior to model training via stop word removal, lemmatization, named entity masking, and coocurrence modeling.
+                """
+            )
             gr.Markdown(
-                """#### App usage:
-                Add your input prompts to the text field on the right. To use multiple input prompts at once separate
-                them by comma, semicolon or a new line
-                ##### Examples
-                - Climate change
-                - Financial risk, energy dependency, climate neutrality
+                """
+                #### App usage
+                The model is intented to be used for **semantic search**: It encodes the search query (entered in the textbox on the right) in a dense vector space and finds semantic neighbours, i.e., token which frequently occur within similar contexts in the underlying training data.
+                The model allows for two use cases:
+                1. *Single Search:* The input query consists of a single word. When provided a bi-, tri-, or even fourgram, the quality of the model output depends on the presence of the query token in the model's vocabulary. N-grams should be concated by an underscore (e.g., "machine_learning" or "artifical_intelligence").
+                2. *Multi Search:* The input query may consist of several words or n-grams, seperated by comma, semi-colon or newline. It then computes the average vector over all inputs and performs semantic search based on the average input token.
                 """
             )
         with gr.Column():
-            text_input = gr.Textbox(lines=1)
+            text_in = gr.Textbox(lines=1, placeholder="Insert text", label="Search Query")
             with gr.Row():
-                n_output = gr.Slider(minimum=5, maximum=50, step=1)
-                compute_button = gr.Button("Compute")
-            df_output = gr.Dataframe(interactive=False)
-            file_out = gr.File(interactive=False)
-
-    compute_button.click(process, inputs=[text_input, n_output], outputs=[df_output, file_out, text_input])
+                n = gr.Slider(value=50, minimum=5, maximum=250, step=5, label="Number of Neighbours")
+                compute_bt = gr.Button("Start\nSearch")
+            df_out = gr.Dataframe(interactive=False)
+            f_out = gr.File(interactive=False, label="Download")
+    gr.Examples(
+        examples = [["transformation", 3], ["climate_change", 3], ["risk, political_risk, uncertainty", 5]],
+        inputs = [text_in, n],
+        outputs = [df_out, f_out, text_in],
+        fn = semantic_search,
+        cache_examples=True
+    )
+    gr.Markdown(
+        """
+        <div style='text-align: center;'>Call2Vec by X and Y</center></div>
+        <p class="aligncenter"><img 'id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=simonschoe.call2vec&left_color=green&right_color=blue" /></p>
+        """
+    )
+    compute_bt.click(semantic_search, inputs=[text_in, n], outputs=[df_out, f_out, text_in])
 
-demo.launch()
+app.launch()