Add application file

app.py

@@ -0,0 +1,87 @@
+"""GPT-1 and GPT-2 Text Generation demo."""
+import gradio as gr
+from torch.cuda import is_available
+from transformers import OpenAIGPTLMHeadModel, OpenAIGPTTokenizer, GPT2Tokenizer, GPT2LMHeadModel
+
+
+tokenizer = None
+model = None
+loaded_model = None
+
+
+def load_model(model_name):
+    """Loads the model and tokenizer from HuggingFace."""
+    global tokenizer, model, loaded_model
+    loaded_model = model_name
+    huggingface_model_name = model_name.split('(')[1][:-1]
+    if huggingface_model_name == 'openai-gpt':  # GPT-1
+        tokenizer = OpenAIGPTTokenizer.from_pretrained(huggingface_model_name)
+        model = OpenAIGPTLMHeadModel.from_pretrained(huggingface_model_name)
+    else:  # GPT-2
+        tokenizer = GPT2Tokenizer.from_pretrained(huggingface_model_name)
+        model = GPT2LMHeadModel.from_pretrained(huggingface_model_name)
+    # Load model in CUDA if available
+    if is_available():
+        model = model.cuda()
+
+
+def generate(inp, model_name, temperature, top_p, rep_pty, max_length):
+    """Generates text using the given model and parameters."""
+    if loaded_model != model_name:
+        load_model(model_name)
+    inputs = tokenizer.encode(inp, return_tensors='pt')
+    if is_available():
+        inputs = inputs.cuda()
+    outputs = model.generate(inputs,
+                             max_length=max_length,
+                             temperature=temperature,
+                             num_return_sequences=1,
+                             top_p=top_p,
+                             repetition_penalty=rep_pty)
+    out = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    if 'GPT-1' in model_name:
+        out = out.replace(inp.lower(), "")
+    else:
+        out = out.replace(inp, "")
+    return out
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Try GPT-1 and GPT-2")
+    with gr.Row():
+        with gr.Column(scale=4):
+            inp = gr.Textbox(label="Input text:", placeholder="Write something here.", lines=10,)
+            out = gr.Textbox(label="Generated text:", lines=25)
+        with gr.Column(scale=1):
+            with gr.Row(scale=1):
+                model_name = gr.Dropdown(label="Select a model:",
+                                         choices=['GPT-2 XL (gpt2-xl)',
+                                                  'GPT-2 L (gpt2-large)',
+                                                  'GPT-2 M (gpt2-medium)',
+                                                  'GPT-2 S (gpt2)',
+                                                  'GPT-1 (openai-gpt)'],
+                                         value='GPT-2 XL (gpt2-xl)')
+                btn_run = gr.Button("Generate")
+                temperature = gr.Slider(
+                    label="Temperature",
+                    info=("Degree of randomness in the output, where higher values make it more unpredictable"
+                          " and creative, while lower values make it more deterministic and focused."),
+                    minimum=0.01, maximum=3.0, step=0.01, value=0.7)
+                top_p = gr.Slider(
+                    label="Top-p",
+                    info=("If set to float < 1, only the most probable tokens with probabilities that add up"
+                          " to `top_p` or higher are kept for generation."),
+                    minimum=0.01, maximum=1.0, step=0.01, value=.9)
+                rep_pty = gr.Slider(label="Repetition Penalty",
+                                    info="Token repetition penalty. 1.0 means no penalty.",
+                                    minimum=1.0, maximum=2.0, step=0.01, value=1.2)
+                max_length = gr.Number(label="Max Length",
+                                       info="The maximum length of the sequence to be generated.",
+                                       minimum=1, maximum=1024, value=256, precision=0)
+            # Fill the rest of the column with blank space
+            # (I didn't find a better way to do this)
+            with gr.Row(scale=1000):
+                gr.Markdown()
+    btn_run.click(fn=generate, inputs=[inp, model_name, temperature, top_p, rep_pty, max_length], outputs=out)
+
+demo.launch()