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Update app.py
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import os
import io
import IPython.display
from PIL import Image
import base64
from transformers import pipeline, AutoTokenizer
import requests
import gradio as gr
get_completion = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
def generate_caption(base64_image):
# Decode base64 string to PIL image
image_data = base64.b64decode(base64_image)
image = Image.open(io.BytesIO(image_data))
# Get caption using the BLIP model
caption_result = get_completion(image)
# Ensure a consistent format by always returning a dictionary
if isinstance(caption_result, str):
return {'generated_text': caption_result}
elif caption_result and isinstance(caption_result, list):
return caption_result[0]
else:
return {'generated_text': None}
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 = generate_caption(base64_image)
print(result) # Debugging print statement to see the structure of the result
# Access the 'generated_text' field from the result dictionary
caption_text = result['generated_text']
print(caption_text)
return caption_text
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() # Remove share=True and server_port for Hugging Face Spaces