Update app.py

app.py

@@ -8,16 +8,16 @@ model_large, tokenizer_large = get_model("gpt2-large")
 def predict(inp, model_type):
     if model_type == "gpt2-large":
         model, tokenizer = model_large, tokenizer_large
-        input_ids = tokenizer.encode(inp, return_tensors='tf')
-        beam_output = model.generate(input_ids, max_length=45, num_beams=5,
+        input_ids = tokenizer.encode("user:"+inp+"\nai:", return_tensors='tf')
+        beam_output = model.generate(input_ids, max_length=60, num_beams=5,
                                      no_repeat_ngram_size=2,
                                      early_stopping=True)
         output = tokenizer.decode(beam_output[0], skip_special_tokens=True,
                                   clean_up_tokenization_spaces=True)
     else:
         model, tokenizer = model_small, tokenizer_small
-        input_ids = tokenizer.encode(inp, return_tensors='tf')
-        beam_output = model.generate(input_ids, max_length=60, num_beams=5,
+        input_ids = tokenizer.encode("user:"+inp+"\nai:", return_tensors='tf')
+        beam_output = model.generate(input_ids, max_length=180, num_beams=5,
                                  no_repeat_ngram_size=2, early_stopping=True)
         output = tokenizer.decode(beam_output[0], skip_special_tokens=True,
                               clean_up_tokenization_spaces=True)
@@ -35,7 +35,7 @@ examples = [
     ["The toughest thing about software engineering is", "gpt2-large"],
     ["Is this the real life? Is this just fantasy?", "gpt2-small"]
 ]
-INTERFACE = gradio.Interface(fn=predict, inputs=INPUTS, outputs=OUTPUTS, title="GPT-2",
+INTERFACE = gradio.Interface(fn=predict, inputs=INPUTS, outputs=OUTPUTS, title="Chat GPT-2",
                  description="GPT-2 is a large transformer-based language "
                              "model with 1.5 billion parameters, trained on "
                              "a dataset of 8 million web pages. GPT-2 is "