Update app.py

app.py

@@ -1,32 +1,5 @@
 import gradio as gr
-import numpy as np
-
-gr.Interface.load("models/openai-gpt")
-
-def predict(sentence1, sentence2):
-    sentence_pairs = np.array([[str(sentence1), str(sentence2)]])
-    test_data = BertSemanticDataGenerator(
-        sentence_pairs, labels=None, batch_size=1, shuffle=False, include_targets=False,
-    )
-    probs = model.predict(test_data[0])[0]
-    
-    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
-    return labels_probs
-
-examples = [["Two women are observing something together.", "Two women are standing with their eyes closed."],
-            ["A smiling costumed woman is holding an umbrella", "A happy woman in a fairy costume holds an umbrella"],
-            ["A soccer game with multiple males playing", "Some men are playing a sport"],            
-]
-
-gr.Interface(
-    fn=predict,
-    title="basic with GPT",
-    description = "Natural Language Inference by fine-tuning GPT model",
-    inputs=["text", "text"],
-    examples=examples,
-    #outputs=gr.Textbox(label='Prediction'),
-    outputs=gr.outputs.Label(num_top_classes=3, label='Semantic similarity'),
-    cache_examples=True
-).launch(debug=True, enable_queue=True)
-
-# gr.Interface.load("models/openai-gpt").launch()
+description = "GPT 牛逼"
+examples = [["I am king."]]
+demo = gr.Interface.load("models/bert-base-uncased", description=description, examples=examples)
+demo.launch(share=True)