| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline | |
| model = AutoModelForCausalLM.from_pretrained('cbauer/groupchatGPT', trust_remote_code=True, ignore_mismatched_sizes=True) | |
| tokenizer = AutoTokenizer.from_pretrained('cbauer/groupchatGPT') | |
| generator = TextGenerationPipeline(model=model, tokenizer=tokenizer) | |
| generator = pipeline('text-generation', model=model, trust_remote_code=True) | |
| def generate(text): | |
| result = generator(text, max_length=30, num_return_sequences=1) | |
| return result[0]["generated_text"] | |
| examples = [ | |
| ["###Chase Hello"], | |
| ["### Jake Whats up"], | |
| ] | |
| demo = gr.Interface( | |
| fn=generate, | |
| inputs=gr.inputs.Textbox(lines=5, label="Input Text"), | |
| outputs=gr.outputs.Textbox(label="Generated Text"), | |
| examples=examples | |
| ) | |
| demo.launch() |