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import gradio as gr
from PIL import Image
from transformers import AutoConfig, AutoModelForCausalLM
import torch
# Determine if a GPU is available and set the device accordingly
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load configuration from the base model
config = AutoConfig.from_pretrained("microsoft/Florence-2-base-ft", trust_remote_code=True)
# Load the model using the base model's configuration
model = AutoModelForCausalLM.from_pretrained(
"fauzail/Florence-2-VQA",
config=config,
trust_remote_code=True
).to(device)
from transformers import AutoProcessor
# Load the processor for the model
processor = AutoProcessor.from_pretrained("fauzail/Florence-2-VQA", trust_remote_code=True)
# Define the prediction function for Gradio
def predict(image, question):
inputs = processor(text=[question], images=[image], return_tensors="pt", padding=True).to(device)
outputs = model.generate(**inputs)
return processor.tokenizer.decode(outputs[0], skip_special_tokens=True)
# Create the Gradio interface
interface = gr.Interface(
fn=predict,
inputs=["image", "text"],
outputs="text",
title="Florence 2 VQA - Engineering Drawings",
description="Upload an engineering drawing and ask a related question."
)
# Launch the Gradio interface
interface.launch()
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