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import gradio as gr | |
from uform import gen_model | |
from PIL import Image | |
import requests | |
# Load the model and processor | |
model = gen_model.VLMForCausalLM.from_pretrained("unum-cloud/uform-gen") | |
processor = gen_model.VLMProcessor.from_pretrained("unum-cloud/uform-gen") | |
def generate_caption(image, prompt): | |
# Process the image and the prompt | |
inputs = processor(text=prompt, images=image, return_tensors="pt", padding=True) | |
# Generate the output | |
outputs = model.generate(**inputs) | |
caption = processor.decode(outputs[0], skip_special_tokens=True) | |
return caption | |
# Load the demo image | |
demo_image = Image.open("jungle-glass.png") | |
# Define the Gradio interface | |
iface = gr.Interface( | |
fn=generate_caption, | |
inputs=[ | |
gr.inputs.Image(type="pil", label="Upload Image", default=demo_image), | |
gr.inputs.Textbox(label="Prompt"), | |
], | |
outputs=gr.outputs.Textbox(label="Generated Caption"), | |
) | |
# Launch the interface | |
iface.launch() | |