Update app.py

app.py

@@ -1,16 +1,16 @@
 import gradio as gr
-from transformers import pipeline
+import transformers
 
-model = pipeline("text-classification", model="xlm-roberta-large")
+tokenizer = transformers.AutoTokenizer.from_pretrained("xlm-roberta-large")
+model = transformers.AutoModelForSequenceClassification.from_pretrained("xlm-roberta-large", num_labels=2)
 
-def decision_making(option1, option2):
-    options = [option1, option2]
-    result = model(options, return_all_scores=True)
-    if result[0]['score'] > result[1]['score']:
-        return f"The AI chooses {option1}."
-    else:
-        return f"The AI chooses {option2}."
+def predict(first_option, second_option):
+    input_ids = tokenizer.encode(first_option, second_option, return_tensors="pt", truncation=True, padding=True)
+    output = model(input_ids)[0]
+    result = torch.argmax(output)
+    return first_option if result == 0 else second_option
 
-iface = gr.Interface(fn=decision_making, inputs=["text", "text"], outputs="text", title="Decision Making with XLM-RoBERTa-Large",
-                     description="Enter two options and the AI will choose one.")
-iface.launch()
+inputs = [gr.inputs.Textbox(label="Option 1"), gr.inputs.Textbox(label="Option 2")]
+output = gr.outputs.Textbox(label="Chosen Option")
+interface = gr.Interface(fn=predict, inputs=inputs, outputs=output, title="Decision Making with XLM-Roberta-Large", description="Input your two options and let XLM-Roberta-Large choose one.")
+interface.launch()