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import os
import gradio as gr
import torch
import PIL
from flamingo_mini import FlamingoConfig, FlamingoModel, FlamingoProcessor
EXAMPLES_DIR = 'examples'
DEFAULT_PROMPT = "<image>"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = FlamingoModel.from_pretrained('dhansmair/flamingo-mini')
model.to(device)
model.eval()
processor = FlamingoProcessor(model.config, load_vision_processor=True)
# setup some example images
examples = []
if os.path.isdir(EXAMPLES_DIR):
for file in os.listdir(EXAMPLES_DIR):
path = EXAMPLES_DIR + "/" + file
examples.append([path, DEFAULT_PROMPT])
def predict_caption(image, prompt):
assert isinstance(prompt, str)
features = processor.extract_features(image).to(device)
caption = model.generate_captions(processor,
visual_features=features,
prompt=prompt)
if isinstance(caption, list):
caption = caption[0]
return caption
iface = gr.Interface(fn=predict_caption,
inputs=[gr.Image(type="pil"), gr.Textbox(value=DEFAULT_PROMPT, label="Prompt")],
examples=examples,
outputs="text")
iface.launch()