| import gradio as gr | |
| from transformers import pipeline | |
| classifier = pipeline("text-classification", model="tugot17/my-awesome-model", tokenizer=tokenizer) | |
| def predict(text): | |
| output = {} | |
| for result in classifier(text, top_k=None): | |
| output[result["label"]] = result["score"] | |
| return output | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs='text', | |
| outputs=gr.Label(num_top_classes=2), | |
| examples=[["SIX chances to win CASH! From 100 to 20,000 pounds txt> CSH11 and send to 87575. Cost 150p/day, 6days, 16+ TsandCs apply Reply HL 4 info"]] | |
| ) | |
| iface.launch() |