add gradio model

app.py

@@ -0,0 +1,58 @@
+import gradio as gr
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from model import Transformer
+
+# hyperparameters
+batch_size = 16 # how many independent sequences will we process in parallel?
+block_size = 64 # what is the maximum context length for predictions?
+max_iters = 5000
+eval_interval = 100
+learning_rate = 1e-3
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+eval_iters = 200
+n_embd = 128
+n_head = 8
+n_layer = 4
+dropout = 0.0
+vocab = 101
+# ------------
+
+with open('/Users/deepaksharma/Documents/Python/Kaggle/GenerateKanyeLyrics/Kanye West Lyrics.txt','r',encoding='utf-8') as f:
+    text = f.read()
+
+chars = sorted(list(set(text)))
+
+stoi = {ch:i for i,ch in enumerate(chars)}
+itos = {i:ch for i,ch in enumerate(chars)}
+
+encode = lambda s: [stoi[c] for c in s]
+decode = lambda l: ''.join([itos[c] for c in l])
+
+
+model = Transformer(n_embd,n_layer)
+model.load_state_dict(torch.load('model_weights.pth'))
+model.eval()
+
+def generate_kanye_lyrics(text, max_tokens=500):
+    if len(text)<64:
+        initial_text = ""
+        padding = 64-len(text)
+        initial_list = []
+        for i in range(0, padding):
+            initial_list.append(0)
+        context = initial_list + encode(text)
+    else:
+        padding = 0
+        initial_text = text[0:len(text)-block_size]
+        context = text[-block_size:]
+        context = encode(context)
+    context = torch.tensor(context, dtype=torch.long)
+    lyrics = torch.stack([context for _ in range(1)], dim=0)
+    return initial_text + decode(model.generate(lyrics, max_tokens=int(max_tokens))[0].tolist())[padding:]
+
+demo = gr.Interface(fn=generate_kanye_lyrics, inputs=[gr.Textbox(lines=2, placeholder="Enter Starting lyrics ..."),gr.Number()], outputs="text")
+
+demo.launch()