Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+"""import gradio as gr
 import onnxruntime as rt
 from transformers import AutoTokenizer
 import torch, json
@@ -23,4 +23,47 @@ def classify_movie_genre(sinopse):
 
 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
+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))) 
+
+
+app_examples = [
+    ["asasasa"],
+    ["ddddd"],
+    ["fffff"],
+    ["ggggg"],
+    ["aaaaaa"]
+    
+]
+
+inputs = [
+    gr.Textbox(label="text", value=app_examples[0][0]),
+
+]
+
+
+label = gr.outputs.Label(num_top_classes=4)
+iface = gr.Interface(fn=classify_movie_genre, inputs=inputs, outputs=label, examples=app_examples)
 iface.launch(inline=False)