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| from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig | |
| import torch | |
| import torch.nn.functional as F | |
| import gradio as gr | |
| model_dir = "ZhouBencheng/text-classification" | |
| config = AutoConfig.from_pretrained(model_dir, num_labels=3, finetuning_task="text-classification") | |
| tokenizer = AutoTokenizer.from_pretrained(model_dir) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_dir, config=config) | |
| def inference(input_text): | |
| inputs = tokenizer.batch_encode_plus( | |
| [input_text], | |
| max_length=512, | |
| truncation=True, | |
| padding="max_length", | |
| return_tensors="pt", | |
| ) | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| predicted_class_id = logits.argmax().item() | |
| output = model.config.id2label[predicted_class_id] | |
| return output | |
| with gr.Blocks(css=""" | |
| .message,svelte-w6rprc,svelte-w6rprc,svelte-w6rprc (font-size:20px; margin-top: 20px} | |
| #component-21 >div.wrap.svelte-w6rprc (height: 600px;} | |
| """) as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_text = gr.Textbox(placeholder="Insert your prompt here:", scale=2, container=False) | |
| answer = gr.Textbox(lines=0, label="Answer") | |
| generate_bt = gr.Button("Generate", scale=1) | |
| inputs = [input_text] | |
| outputs = [answer] | |
| generate_bt.click( | |
| fn=inference, inputs=inputs, outputs=outputs, show_progress=True | |
| ) | |
| examples = [ | |
| ["My last two weather pics from the storm on August 2nd. People packed up realfastafter the temp dropped and winds picked up."], | |
| ["Lying clinton sinking! Donald Trump singing: Let's Make America Great Again!"], | |
| ] | |
| demo.queue() | |
| demo.launch() | |