Update app.py

app.py

@@ -7,6 +7,20 @@ def isNoneWords(word):
         return True
     else:
         return False
+    
+def word_analogy(word1, word2, word3):
+    analogy_words = model.similar_by_vector(model.word_vec(word1) - model.word_vec(word2) + model.word_vec(word3))
+    sim_res = ""
+    for item in analogy_words:
+        sim_res += f'{item[0]}: {round(item[1], 4)}\n'
+    return sim_res
+    
+def similarity_route(word1, word2):
+    if isNoneWords(word1) or isNoneWords(word2):
+        return "word is null or not in model!"
+    else:
+        return float(model.similarity(word1, word2))
+
 
 def top_similarity_route(word):
     if isNoneWords(word):
@@ -18,10 +32,90 @@ def top_similarity_route(word):
             sim_res += f'{item[0]}: {round(item[1], 4)}\n'
         return sim_res
 
+def top_similar_words_layout():
+    with gr.Column():
+        with gr.Row():
+            with gr.Column():
+                word = gr.Textbox(lines=1, label='Input word', placeholder='Input word here')
+                with gr.Row():
+                    clear = gr.ClearButton()
+                    submit = gr.Button("Submit")
+            output = gr.Textbox(lines=20, label='Similar words', placeholder='Output here')
 
-if __name__ == '__main__':
-    model = KeyedVectors.load_word2vec_format('tencent-ailab-embedding-zh-d100-v0.2.0-s.txt', binary=False)
+    submit.click(fn=top_similarity_route, inputs=[word], outputs=[output])
+    
+    examples=[['兔子', '松鼠']]
+    ex = gr.Examples(
+        examples=examples,
+        fn=top_similarity_route,
+        inputs=[word],
+        outputs=[output],
+        cache_examples=False, 
+        run_on_click=False
+    )
+    
     
+def similarity_layout():
+    with gr.Column():
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    word1 = gr.Textbox(lines=1, label='Input word1', placeholder='Input word1 here')
+                    word2 = gr.Textbox(lines=1, label='Input word2', placeholder='Input word2 here')
+                with gr.Row():
+                    clear = gr.ClearButton()
+                    submit = gr.Button("Submit")
+            output = gr.Textbox(lines=1, label='Similar words', placeholder='Output here')
+
+    submit.click(fn=similarity_route, inputs=[word1, word2], outputs=[output])
+    
+    examples=[['淘宝', '京东', 0.7887385]]
+    ex = gr.Examples(
+        examples=examples,
+        fn=similarity_route,
+        inputs=[word1, word2],
+        outputs=[output],
+        cache_examples=False, 
+        run_on_click=False
+    )
+    
+def word_analogy_layout():
+    with gr.Column():
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    word1 = gr.Textbox(lines=1, label='Input word1', placeholder='Input word1 here')
+                    word2 = gr.Textbox(lines=1, label='Input word2', placeholder='Input word2 here')
+                    word3 = gr.Textbox(lines=1, label='Input word3', placeholder='Input word3 here')
+                with gr.Row():
+                    clear = gr.ClearButton()
+                    submit = gr.Button("Submit")
+            output = gr.Textbox(lines=1, label='Analogy words', placeholder='Output here')
+
+    submit.click(fn=word_analogy, inputs=[word1, word2, word3], outputs=[output])
+    
+    examples=[['国王', '男人', '女人', '王后']]
+    ex = gr.Examples(
+        examples=examples,
+        fn=word_analogy,
+        inputs=[word1, word2, word3],
+        outputs=[output],
+        cache_examples=False, 
+        run_on_click=False
+    )
+
+if __name__ == '__main__':
+    model = KeyedVectors.load_word2vec_format('../word_sim_demo/tencent-ailab-embedding-zh-d100-v0.2.0-s/tencent-ailab-embedding-zh-d100-v0.2.0-s.txt', binary=False)
     title = 'Calculate word similarity based on Tencent AI Lab Embedding'
-    iface = gr.Interface(fn=top_similarity_route, inputs="text", outputs="text", title=title)
-    iface.launch()
+
+    with gr.Blocks() as demo:
+        gr.HTML(title)
+        with gr.Column(elem_id="col-container"):
+            with gr.Tab("Top similar words"):
+                top_similar_words_layout()
+            with gr.Tab("Similarity of words"):
+                similarity_layout()
+            with gr.Tab("Word analogy"):
+                word_analogy_layout()
+            
+    demo.queue(max_size=64).launch()