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import os
from transformers import BlipProcessor, BlipForConditionalGeneration
import gradio as gr
# Load the token from the environment
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
# Load the model and processor with the token
processor = BlipProcessor.from_pretrained(
"quadranttechnologies/Imageclassification",
use_auth_token=HUGGINGFACE_TOKEN
)
model = BlipForConditionalGeneration.from_pretrained(
"quadranttechnologies/Imageclassification",
use_auth_token=HUGGINGFACE_TOKEN
)
# Define your Gradio interface and logic as before
def generate_caption(image):
try:
inputs = processor(image, return_tensors="pt")
outputs = model.generate(**inputs)
caption = processor.decode(outputs[0], skip_special_tokens=True)
return caption
except Exception as e:
return f"Error generating caption: {e}"
interface = gr.Interface(
fn=generate_caption,
inputs=gr.Image(type="pil"),
outputs="text",
title="Image Captioning Model",
description="Upload an image to receive a caption generated by the model."
)
if __name__ == "__main__":
interface.launch(share=True)
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