Update app.py

app.py

@@ -1,12 +1,13 @@
 import gradio as gr
 import onnxruntime as rt
 from transformers import AutoTokenizer
-import torch, json
+import torch
+import json
 
 tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
 
 with open("tag_types_encoded(1).json", "r") as fp:
-  encode_genre_types = json.load(fp)
+    encode_genre_types = json.load(fp)
 
 genres = list(encode_genre_types.keys())
 
@@ -15,13 +16,15 @@ input_name = inf_session.get_inputs()[0].name
 output_name = inf_session.get_outputs()[0].name
 
 def classify_Quote_tag(Quote):
-  input_ids = tokenizer(Quote)['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))) 
+    input_ids = tokenizer(Quote)['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_Quote_tag, inputs="text", outputs=label)
-iface.launch(inline=False)
-					
+iface = gr.Interface(
+    fn=classify_Quote_tag,
+    inputs="text",
+    outputs=gr.Label(num_top_classes=5)  # Use gr.Label for the label output
+)
+iface.launch(inline=False)