Add application file

app.py

@@ -1,8 +1,7 @@
 import gradio as gr
 import onnxruntime as rt 
 from transformers import AutoTokenizer
-import torch
-import json
+import torch, json
 
 tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
 
@@ -15,17 +14,14 @@ inf_session = rt.InferenceSession('movie-classifier.onnx')
 input_name = inf_session.get_inputs()[0].name
 output_name = inf_session.get_outputs()[0].name
 
-def classify_movie_genre(Overview, num_top_classes=5):
+def classify_movie_genre(Overview):
     input_ids = tokenizer(Overview)['input_ids'][:512]
     logits = inf_session.run([output_name], {input_name: [input_ids]})[0]
     logits = torch.FloatTensor(logits)
     probs = torch.sigmoid(logits)[0]
-    
-    # Get the top N predicted genres
-    top_genres = [genres[i] for i in probs.argsort(descending=True)[:num_top_classes]]
-    
-    return top_genres
+    return dict(zip(genres, map(float, probs)))
 
-label = gr.outputs.Label()
+label = gr.Interface.Label(num_top_classes=5)  # Use Label() instead of gr.outputs.Label()
 iface = gr.Interface(fn=classify_movie_genre, inputs="text", outputs=label)
 iface.launch(inline=False)
+