Update app.py

app.py

@@ -1,9 +1,9 @@
-from gradio import components as grc
+import gradio as gr
 import onnxruntime as rt
 from transformers import AutoTokenizer
 import torch, json
 
-tokenizer = AutoTokenizer.from_pretrained("neuralmind/bert-large-portuguese-cased")
+tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
 
 with open("genre_types_encoded.json", "r") as fp:
   encode_genre_types = json.load(fp)
@@ -14,14 +14,13 @@ 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(description):
-  input_ids = tokenizer(description)['input_ids'][:512]
+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 = grc.Label(num_top_classes=5)
-iface = grc.Interface(fn=classify_movie_genre, inputs="text", outputs=label)
+label = gr.outputs.Label(num_top_classes=5)
+iface = gr.Interface(fn=classify_movie_genre, inputs="text", outputs=label)
 iface.launch(inline=False)
-