my-first-gradio / app.py
cantremember's picture
okay. base Gradio example works. now for the lesson 14 contents
fec5389
raw
history blame contribute delete
389 Bytes
import gradio as gr
from transformers import pipeline
pipe = pipeline("image-to-text",
model="Salesforce/blip-image-captioning-base")
def launch(input):
out = pipe(input)
return out[0]['generated_text']
iface = gr.Interface(launch,
inputs=gr.Image(type='pil'),
outputs="text")
# iface.launch(share=True)
iface.launch()