Update app.py

app.py

@@ -1,29 +1,29 @@
-import gradio as gr
-import onnxruntime as rt
-from transformers import AutoTokenizer
-import torch, json
-
-tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
-
-with open("genre_types_encoded_multi_class.json", "r") as fp:
-  encode_genre_types = json.load(fp)
-
-genres = list(encode_genre_types.keys())
-
-
-inf_session = rt.InferenceSession('paper_task_classifier_quantized.onnx')
-input_name = inf_session.get_inputs()[0].name
-output_name = inf_session.get_outputs()[0].name
-
-
-def classify_film_genre(description):
-  input_ids = tokenizer(description)['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_film_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("distilroberta-base")
+
+with open("genre_types_encoded_multi_class.json", "r") as fp:
+  encode_genre_types = json.load(fp)
+
+genres = list(encode_genre_types.keys())
+
+
+inf_session = rt.InferenceSession('film_genre_classifier_quantized.onnx')
+input_name = inf_session.get_inputs()[0].name
+output_name = inf_session.get_outputs()[0].name
+
+
+def classify_film_genre(description):
+  input_ids = tokenizer(description)['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_film_genre, inputs="text", outputs=label)
+iface.launch(inline=False)
+