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import torch
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import gradio as gr
# Step 1: Load the processor from Hugging Face
processor = AutoProcessor.from_pretrained("microsoft/git-large-textcaps")
# Step 2: Load the model architecture from Hugging Face
model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-textcaps") # Load model structure
# Step 3: Load your custom PyTorch weights
custom_weights_path = "model_folder/pytorch_model.bin" # Path to your custom weights
model.load_state_dict(torch.load(custom_weights_path, map_location=torch.device("cpu"))) # Load custom weights
model.eval() # Set the model to evaluation mode
# Step 4: 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
# Step 5: 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
)
# Step 6: Launch the Gradio app
interface.launch()
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