| import gradio as gr | |
| import OS | |
| os.system("pip install transformers sentencepiece torch") | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("yniw/open-calm-7b-4gib") | |
| model = AutoModelForCausalLM.from_pretrained("yniw/open-calm-7b-4gib") | |
| def generate_text(input_text, temperature=0.8, max_length=20): | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt") | |
| output = model.generate(input_ids, max_length=max_length, temperature=temperature) | |
| generated_text = tokenizer.decode(output[0], skip_special_tokens=True) | |
| return generated_text | |
| inputs = gr.inputs.Textbox(lines=2, label="Input Text") | |
| temperature = gr.inputs.Slider(minimum=0.2, maximum=1.0, default=0.8, step=0.1, label="Temperature") | |
| max_length = gr.inputs.Slider(minimum=10, maximum=50, default=20, step=5, label="Max Length") | |
| output_text = gr.outputs.Textbox(label="Generated Text") | |
| interface = gr.Interface(fn=generate_text, inputs=[inputs, temperature, max_length], outputs=output_text, title="Text Generation Interface") | |
| interface.launch() | |