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import torch
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
import gradio as gr
# Load the BLIP model and processor
try:
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
model = model.to(device='cuda' if torch.cuda.is_available() else 'cpu')
model.eval()
except Exception as e:
print(f"Error loading model or processor: {e}")
exit()
def process_image(image, question):
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Convert Gradio image to PIL Image
image = Image.fromarray(image).convert('RGB')
# Preprocess the image and question
inputs = processor(image, question, return_tensors="pt").to(device)
# Perform inference
try:
with torch.no_grad():
outputs = model.generate(**inputs)
answer = processor.decode(outputs[0], skip_special_tokens=True)
return answer
except Exception as e:
return f"Error during model inference: {e}"
# Define the Gradio interface
interface = gr.Interface(
fn=process_image,
inputs=[gr.Image(type='numpy'), gr.Textbox(label="Question")],
outputs=gr.Textbox(),
title="Image Question Answering",
description="Upload an image and ask a question about it. The model will provide an answer."
)
# Launch the Gradio app
interface.launch()
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