# 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()