| # import torch | |
| # from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # import gradio as gr | |
| # # Load model and tokenizer (using CPU for broader accessibility) | |
| # model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float32, device_map="cpu", trust_remote_code=True) | |
| # tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True) | |
| # def generate_text(prompt): | |
| # inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False) | |
| # outputs = model.generate(**inputs, max_length=200) | |
| # text = tokenizer.batch_decode(outputs)[0] | |
| # return text | |
| # # Create Gradio interface | |
| # iface = gr.Interface( | |
| # fn=generate_text, | |
| # inputs=[gr.Textbox(lines=5, label="Enter your prompt")], | |
| # outputs="text", | |
| # title="PHI-2 Text Generator", | |
| # description="Generate text using the PHI-2 generative language model", | |
| # ) | |
| # # Launch the interface | |
| # iface.launch() | |
| import gradio as gr | |
| from transformers import pipeline | |
| pipe = pipeline("text2text-generation", model="yeye776/t5-OndeviceAI-HomeIoT") | |
| # gr.load("models/yeye776/t5-OndeviceAI-HomeIoT").launch() | |
| iface = gradio.Interface(fn=pipe, inputs="text", outputs="text") | |
| iface.launch() |