Create app.py

app.py

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+import gradio as gr
+from gpt import GPTLanguageModel
+import torch
+import config as cfg
+
+torch.manual_seed(1337)
+
+
+with open('input.txt', 'r', encoding='utf-8') as f:
+    text = f.read()
+
+
+chars = sorted(list(set(text)))
+vocab_size = len(chars)
+
+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[i] for i in l])
+
+model = GPTLanguageModel(vocab_size)
+model.load_state_dict(torch.load('saved_model.pth', map_location=cfg.device))
+m = model.to(cfg.device)
+
+def inference(input_text, count):
+    encoded_text = [encode(input_text)]
+    count = int(count)
+    context = torch.tensor(encoded_text, dtype=torch.long, device=cfg.device)
+     
+    out_text = decode(m.generate(context, max_new_tokens=count)[0].tolist())
+    return out_text
+
+title = "ERAV1 Session 21: Training GPT from scratch"
+ 
+
+demo = gr.Interface(
+    inference, 
+    inputs = [gr.Textbox(label="Text", placeholder="Enter text"), gr.Textbox(label="Tokens", placeholder="Enter number of tokens to be generated")], 
+    outputs = [gr.Textbox(label="Generated text")],
+    title = title
+)
+
+demo.launch()