Update app.py

app.py

@@ -1,7 +1,72 @@
 import gradio as gr
+from model import GPTConfig, GPT
+import torch              
 
-def greet(name):
-    return "Hello " + name + "!!"
+def remove_caseifer(text):
+    new_text = ""
+    i = 0
+    while i < len(text):
+        if text[i] == "^":
+            if i+1 < len(text):
+                new_text += text[i+1].upper()
+                i += 1
+            else:
+                pass  # skip this index
+        else:
+            new_text += text[i]
+        i += 1
+    return new_text
+    
+def add_caseifer(text):
+    new_text = ""
+    for char in text:
+        if char.isupper():
+            new_text += "^" + char.lower()
+        else:
+            new_text += char
+    return new_text
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+max_new_tokens = 175 # number of tokens generated in each sample
+temperature = 0.8 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
+top_k = 200 # retain only the top_k most likely tokens, clamp others to have 0 probability
+device = 'cuda' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
+
+
+torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
+torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
+device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast
+ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
+ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
+
+# init from a model saved in a specific directory
+ckpt_path = os.path.join(out_dir, 'ckpt.pt')
+checkpoint = torch.load(ckpt_path, map_location=device)
+gptconf = GPTConfig(**checkpoint['model_args'])
+model = GPT(gptconf)
+state_dict = checkpoint['model']
+unwanted_prefix = '_orig_mod.'
+for k,v in list(state_dict.items()):
+    if k.startswith(unwanted_prefix):
+        state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+model.load_state_dict(state_dict)
+
+model.eval()
+model.to(device)
+
+meta_path = os.path.join(out_dir, 'meta.pkl')
+load_meta = os.path.exists(meta_path)
+
+with open(meta_path, 'rb') as f:
+    meta = pickle.load(f)
+# TODO want to make this more general to arbitrary encoder/decoder schemes
+stoi, itos = meta['stoi'], meta['itos']
+encode = lambda s: [stoi[c] for c in s]
+decode = lambda l: ''.join([itos[i] for i in l])
+
+def gen(input)
+    start_ids = encode(add_caseifer(input))
+    x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
+    y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
+    return remove_caseifer(decode(y[0].tolist()))
+iface = gr.Interface(fn=gen, inputs="text", outputs="text")
 iface.launch()