Update app.py

app.py

@@ -1,30 +1,3 @@
-"""import gradio as gr
-import onnxruntime as rt
-from transformers import AutoTokenizer
-import torch, json
-
-tokenizer = AutoTokenizer.from_pretrained("neuralmind/bert-large-portuguese-cased")
-
-with open("genre_types_encoded.json", "r") as fp:
-  encode_genre_types = json.load(fp)
-
-genres = list(encode_genre_types.keys())
-
-inf_session = rt.InferenceSession('movie-classifier-quantized.onnx')
-input_name = inf_session.get_inputs()[0].name
-output_name = inf_session.get_outputs()[0].name
-
-def classify_movie_genre(sinopse):
-  input_ids = tokenizer(sinopse)['input_ids'][:512]
-  logits = inf_session.run([output_name], {input_name: [input_ids]})[0]
-  logits = torch.FloatTensor(logits)
-  probs = torch.sigmoid(logits)[0]
-  return dict(zip(genres, map(float, probs))) 
-
-label = gr.outputs.Label(num_top_classes=5)
-iface = gr.Interface(fn=classify_movie_genre, inputs="text", outputs=label)
-iface.launch(inline=False)"""
-
 import gradio as gr
 import onnxruntime as rt
 from transformers import AutoTokenizer
@@ -71,5 +44,5 @@ inputs = [
 
 
 label = gr.outputs.Label(num_top_classes=4)
-iface = gr.Interface(fn=classify_movie_genre, inputs=inputs, outputs=label, examples=app_examples)
+iface = gr.Interface(fn=classify_movie_genre, inputs=inputs, outputs=label, examples=app_examples, theme='gradio/monochrome')
 iface.launch()