Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+
+model = None
+def semantic(company_1, company_2):
+    # Single list of sentences
+    sentences = [company_1, company_2]
+    if model is None:
+        model = SentenceTransformer('all-mpnet-base-v2')
+    #Compute embeddings
+    embeddings = model.encode(sentences, convert_to_tensor=True)
+    #Compute cosine-similarities for each sentence with each other sentence
+    cosine_scores = util.cos_sim(embeddings, embeddings)
+    #Find the pairs with the highest cosine similarity scores
+    pairs = []
+    for i in range(len(cosine_scores)-1):
+        for j in range(i+1, len(cosine_scores)):
+            pairs.append({'index': [i, j], 'score': cosine_scores[i][j]})
+    #Sort scores in decreasing order
+    pairs = sorted(pairs, key=lambda x: x['score'], reverse=True)
+    for pair in pairs:
+        return "{:.4f}".format(pair['score'])
+    
+company_1 = "Growth Capital Acquisition Corp"
+company_2 = "Growth Capital Acquisition Corp III"
+
+title = 'sentences_semantic'
+gr.Interface(semantic,inputs=[gr.inputs.Textbox(lines=1, default=company_1, label="Company_1"), gr.inputs.Textbox(lines=1, default=company_2, label="Company_2")],
+             outputs=[gr.outputs.Textbox(type="auto",label="Answer")],title = title).launch()