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app.py

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+import gradio as gr
+import onnxruntime as rt
+from transformers import AutoTokenizer
+import torch, json
+
+tokenizer = AutoTokenizer.from_pretrained("roberta-base")
+
+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('genres-classifier-quantized.onnx')
+input_name = inf_session.get_inputs()[0].name
+output_name = inf_session.get_outputs()[0].name
+
+def classify_movietvshow_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_movietvshow_genre, inputs="text", outputs=label)
+iface.launch(inline=False)
+					

genre_types_encoded.json

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+{"Action": 0, "Thriller": 1, "Drama": 2, "Crime": 3, "Sport": 4, "Comedy": 5, "Mystery": 6, "Biography": 7, "Documentary": 8, "Sci-Fi": 9, "Romance": 10, "Western": 11, "Fantasy": 12, "Horror": 13, "Animation": 14, "Adventure": 15, "Family": 16, "History": 17, "War": 18, "Musical": 19, "Music": 20, "Reality-TV": 21, "Game-Show": 22}

genres-classifier-quantized.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:76313440f31d8176acc13ab4496b44f4c795ee3527dd1faa3f114fb74c427aee
+size 125514820

requirements.txt

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+gradio==4.14.0
+onnxruntime==1.16.3
+torch==1.13.1
+transformers==4.35.2