Delete app.py

app.py

@@ -1,58 +0,0 @@
-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()