gradio app basic works

app.py

@@ -0,0 +1,17 @@
+import gradio as gr
+
+import src.evaluator as evaluator
+
+
+def compress(text):
+    return evaluator.evaluate(text, compression=True)
+
+
+with gr.Blocks() as demo:
+    raw_text = gr.Textbox(label="Raw Text", )
+    compressed_textbox = gr.Textbox(label="Compressed")
+    metadata_textbox = gr.Textbox(label="Report")
+    btn = gr.Button("Compress")
+    btn.click(fn=compress, inputs=raw_text, outputs=[compressed_textbox, metadata_textbox], api_name="compress")
+
+demo.launch()

model_lr0.0001_bs256_epoch50.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31923ca96e3c2471ad6252dfb615b15cde784be5a7792c7379d1c9a9b27a7f4e
+size 551468733

src/evaluator.py

@@ -13,3 +13,18 @@ def evaluate(args):
         print(decompress(args.text, Tokenizer(vocab), model))
     else:
         print(compress(args.text, Tokenizer(vocab), model))
+
+
+def evaluate(text, compression=True):
+    vocab = torch.load("vocab.pt", map_location=torch.device('cpu'))
+    model = Model2(len(vocab), 300, 256, vocab['<PAD>'])
+    load_from_checkpoint(model, "model_lr0.0001_bs256_epoch50.pt")
+
+    if not compression:
+        result = decompress(text, Tokenizer(vocab), model)
+    else:
+        result = compress(text, Tokenizer(vocab), model)
+
+    # calculate the compression ratio from string lengths
+    compression_ratio = (1 - (len(result) / len(text))) * 100
+    return result, f"{compression_ratio}% compressed"