Update app.py

app.py

@@ -1,3 +1,53 @@
-import gradio as gr
+import gradio
+from gpt import get_model
 
-gr.Interface.load("models/gpt2").launch()
+
+model_small, tokenizer_small = get_model("gpt2")
+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,
+                                     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,
+                                 no_repeat_ngram_size=2, early_stopping=True)
+        output = tokenizer.decode(beam_output[0], skip_special_tokens=True,
+                              clean_up_tokenization_spaces=True)
+    if output.count(".") >= 2:
+        output = ".".join(output.split(".")[:-1]) + "."
+    return output
+
+ 
+
+INPUTS = [gradio.inputs.Textbox(lines=2, label="Input Text"),
+            gradio.inputs.Radio(choices=["gpt2-small", "gpt2-large"],
+                                label="Model Size")]
+OUTPUTS = gradio.outputs.Textbox()
+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",
+                 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 "
+                             "trained with a simple objective: predict the "
+                             "next word, given all of the previous words "
+                             "within some text. You can configure small vs "
+                             "large below: the large model takes longer to "
+                             "run ("
+                             "55s vs 30s) "
+                             "but generates better text.",
+                 thumbnail="https://github.com/gradio-app/gpt-2/raw/master/screenshots/interface.png?raw=true",
+                             examples=examples,
+                 capture_session=False)
+
+INTERFACE.launch(inbrowser=True)