import gradio as gr
   import torch
   from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

   MODEL_NAME = "cloudqi/cqi_text_to_image_pt_v0"
   tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
   model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)

   def generate_images(description):
       input_ids = tokenizer.encode(description, return_tensors="pt")
       # Model generates a batch of one image
       output = model.generate(input_ids)
       output_image = output[0].numpy().transpose(1,2,0)
       return output_image.astype("uint8")

   inputs = gr.inputs.Textbox(prompt="Enter Text Description")
   outputs = gr.outputs.Image(label="Generated Image")

   iface = gr.Interface(fn=generate_images, inputs=inputs, outputs=outputs, title="Description to Image")
   iface.disable_caching=True  # Disable caching to ensure the model is reloaded each time the app is opened

   if __name__ == "__main__":
       iface.launch()