app.py

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()