initial working test

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+from sklearn.neighbors import NearestNeighbors
+
+
+
+MODEL = "cardiffnlp/twitter-roberta-base-jun2022"
+model = AutoModel.from_pretrained(MODEL)
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+embedding_matrix = model.embeddings.word_embeddings.weight
+embedding_matrix = embedding_matrix.detach().numpy()
+
+knn_model = NearestNeighbors(n_neighbors=500,
+                         metric='cosine',
+                         algorithm='auto',
+                         n_jobs=3)
+                
+nbrs = knn_model.fit(embedding_matrix)
+
+distances, indices = nbrs.kneighbors(embedding_matrix)
+
+
+title = "How does a word's meaning change with time?"
+
+
+def topk(word):
+    outs = []
+    index = tokenizer.encode(f'{word}')
+    for i in indices[index[1]]:
+        outs.append(tokenizer.decode(i))
+        print(tokenizer.decode(i))
+
+with gr.Blocks() as demo:
+    gr.Markdown(f" # {title}")
+    # gr.Markdown(f" ## {description1}")
+    # gr.Markdown(f"{description2}")
+    # gr.Markdown(f"{description3}")
+    with gr.Row():
+        word = gr.Textbox(label="Word")
+    with gr.Row():
+        greet_btn = gr.Button("Compute")
+    with gr.Row():
+        greet_btn.click(fn=topk, inputs=[word], outputs=gr.outputs.Textbox())
+
+demo.launch()

requirements.txt

@@ -0,0 +1,3 @@
+transformers
+torch
+scikit-learn