Create qa.py

qa.py

@@ -0,0 +1,21 @@
+import torch
+from PIL import Image
+from transformers import Blip2Processor, Blip2ForConditionalGeneration, BitsAndBytesConfig
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
+
+processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
+model = Blip2ForConditionalGeneration.from_pretrained(
+    "Salesforce/blip2-flan-t5-xl", device_map="auto"
+)
+
+def get_image_answer(image: Image.Image, question: str) -> str:
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    inputs = processor(images=image, text=question, return_tensors="pt")
+    inputs = {k: v.to(device, torch.float16 if device == "cuda" else torch.float32) for k, v in inputs.items()}
+    output_ids = model.generate(**inputs)
+    answer = processor.tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
+    return answer