File size: 1,363 Bytes
b9a9c78 95c8975 b9a9c78 95c8975 b9a9c78 95c8975 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import gradio as gr
# Define the folder where the model and processor are saved
saved_folder_path = "model_folder" # Replace with the path to your model folder
# Load processor and model
processor = AutoProcessor.from_pretrained(saved_folder_path) # Processor (e.g., feature extractor + tokenizer)
model = AutoModelForVision2Seq.from_pretrained(saved_folder_path) # Pre-trained BLIP model
model.eval() # Set model to evaluation mode
# Define the caption generation function
def generate_caption(image):
# Convert the input image to PIL format (if necessary)
image = Image.fromarray(image)
# Preprocess the image using the processor
inputs = processor(images=image, return_tensors="pt")
pixel_values = inputs.pixel_values
# Generate caption
generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return generated_caption
# Define the Gradio interface
interface = gr.Interface(
fn=generate_caption, # Function to process input
inputs=gr.Image(), # Input as image
outputs=gr.Textbox(), # Output as text
live=True # Enable live prediction
)
# Launch the Gradio app
interface.launch()
|