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app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+from gpt import GPTLanguageModel
+import torch
+import config as cfg
+
+torch.manual_seed(1337)
+
+# wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
+with open('input.txt', 'r', encoding='utf-8') as f:
+    text = f.read()
+
+# here are all the unique characters that occur in this text
+chars = sorted(list(set(text)))
+vocab_size = len(chars)
+# create a mapping from characters to integers
+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] # encoder: take a string, output a list of integers
+decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
+
+model = GPTLanguageModel(vocab_size)
+model.load_state_dict(torch.load('saved_model.pth'))
+m = model.to(cfg.device)
+
+def inference(input_context, count):
+    encoded_text = [encode(input_context)]
+    count = int(count)
+    context = torch.tensor(encoded_text, dtype=torch.long, device=cfg.device)
+     
+    print('--------------------context = ',context)
+    out_text = decode(m.generate(context, max_new_tokens=count)[0].tolist())
+    return out_text
+
+title = "TSAI S21 Assignment: GPT training on mini shakespeare dataset"
+description = "A simple Gradio interface that accepts a context and generates shakespere like text "
+ 
+
+demo = gr.Interface(
+    inference, 
+    inputs = [gr.Textbox(placeholder="Enter starting characters"), gr.Textbox(placeholder="Enter number of characters you want to generate")], 
+    outputs = [gr.Textbox(label="Shakespeare like generated text")],
+    title = title,
+    description = description
+)
+
+demo.launch()
+

saved_model.pth

@@ -1,3 +1,3 @@
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