app change

app.py

@@ -1,7 +1,27 @@
 import gradio as gr
+import onnxruntime as rt
+from transformers import AutoTokenizer
+import torch, json
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
+
+with open("label_types_encoded.json", "r") as fp:
+    encode_genre_types = json.load(fp)
+
+inf_session = rt.InferenceSession('food-classifier-quantized.onnx')
+input_name = inf_session.get_inputs()[0].name
+output_name = inf_session.get_outputs()[0].name
+
+
+def classify_news_label(article):
+    input_ids = tokenizer(article)['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(label, map(float, probs)))
+
+
+label = gr.outputs.Label(num_top_classes=6)
+iface = gr.Interface(fn=classify_news_label, inputs="text", outputs=label)
+iface.launch(inline=False)