Create app.py

app.py

@@ -0,0 +1,51 @@
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import torch
+tokenizer = AutoTokenizer.from_pretrained("af1tang/personaGPT")
+model = AutoModelForCausalLM.from_pretrained("af1tang/personaGPT")
+if torch.cuda.is_available():
+    model = model.cuda()
+## utility functions ##
+flatten = lambda l: [item for sublist in l for item in sublist]
+
+def to_data(x):
+    if torch.cuda.is_available():
+        x = x.cpu()
+    return x.data.numpy()
+
+def to_var(x):
+    if not torch.is_tensor(x):
+        x = torch.Tensor(x)
+    if torch.cuda.is_available():
+        x = x.cuda()
+    return x
+
+def display_dialog_history(dialog_hx):
+    for j, line in enumerate(dialog_hx):
+        msg = tokenizer.decode(line)
+        if j %2 == 0:
+            print(">> User: "+ msg)
+        else:
+            print("Bot: "+msg)
+            print()
+
+def generate_next(bot_input_ids, do_sample=True, top_k=10, top_p=.92,
+                  max_length=1000, pad_token=tokenizer.eos_token_id):
+    full_msg = model.generate(bot_input_ids, do_sample=True,
+                                              top_k=top_k, top_p=top_p, 
+                                              max_length=max_length, pad_token_id=tokenizer.eos_token_id)
+    msg = to_data(full_msg.detach()[0])[bot_input_ids.shape[-1]:]
+    return msg
+
+
+                      
+                      
+                      
+# get personality facts for conversation
+personas = []
+for i in range(3):
+    response = input(">> Fact %d: "%(i+1))+ tokenizer.eos_token
+    personas.append(response)
+personas = tokenizer.encode(''.join(['<|p2|>'] + personas + ['<|sep|>'] + ['<|start|>']))
+
+
+