app.py

import gradio as gr 

# def image_to_base64_str(pil_image):
#     byte_arr = io.BytesIO()
#     pil_image.save(byte_arr, format='PNG')
#     byte_arr = byte_arr.getvalue()
#     return str(base64.b64encode(byte_arr).decode('utf-8'))

# def captioner(image):
#     base64_image = image_to_base64_str(image)
#     result = get_completion(base64_image)
#     return result[0]['generated_text']

from transformers import pipeline
import gradio as gr

pipeline = pipeline("image-classification", model="google/vit-base-patch16-224")
gr.Interface.from_pipeline(pipeline).launch()

gr.close_all()
# demo = gr.Interface(fn=captioner,
#                     inputs=[gr.Image(label="Upload image", type="pil")],
#                     outputs=[gr.Textbox(label="Caption")],
#                     title="Image Captioning with BLIP",
#                     description="Caption any image using the BLIP model",
#                     allow_flagging="never",
#                     examples=["christmas_dog.jpeg", "bird_flight.jpeg", "cow.jpeg"])

# demo.launch(share=True, server_port=int(os.environ['PORT1']))