Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import torch
+import numpy as np
+from model import Transformer
+from transformers import AutoTokenizer  # pip install transformers
+from utils import (
+    BLOCK_SIZE,
+    DEVICE,
+    DROPOUT,
+    NUM_EMBED,
+    NUM_HEAD,
+    NUM_LAYER,
+    encode,
+    decode
+)
+
+#tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/rubert-base-cased")
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+vocab_size = tokenizer.vocab_size
+
+# train a new model
+model = Transformer(
+    vocab_size=vocab_size,
+    num_embed=NUM_EMBED,
+    block_size=BLOCK_SIZE,
+    num_heads=NUM_HEAD,
+    num_layers=NUM_LAYER,
+    dropout=DROPOUT
+)
+# load model to GPU if available
+m = model.to(DEVICE)
+# print the number of parameters in the model
+
+m = torch.load("base_model.pth", map_location=torch.device(DEVICE))
+m.eval()
+
+#print(
+#    "Model with {:.2f}M parameters".format(sum(p.numel() for p in m.parameters()) / 1e6)
+#)
+def model_generate(text):
+    # generate some output based on the context
+    #context = torch.tensor(np.array(encode("Hello! My name is ", tokenizer)))
+    #context = torch.zeros((1, 1), dtype=torch.long, device=DEVICE)
+    text_input = str(input())
+    context_np = np.array(encode(text_input, tokenizer))
+    context_np = np.array([context_np])
+    context = torch.from_numpy(context_np)
+    #print(context)
+
+    return decode(enc_sec=m.generate(idx=context, max_new_tokens=100, block_size=BLOCK_SIZE)[0], tokenizer=tokenizer)
+
+iface = gr.Interface(fn=model_generate, inputs="text", outputs="text")
+iface.launch()