Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import BlipForQuestionAnswering, AutoProcessor
+from PIL import Image
+import spaces
+
+# Check device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model and processor
+model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(device)
+processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
+
+@spaces.GPU  # ZeroGPU: Allocate GPU only when generating images
+def answer_question(image, question):
+    inputs = processor(image, question, return_tensors="pt").to(device)
+    out = model.generate(**inputs)
+    return processor.decode(out[0], skip_special_tokens=True)
+
+iface = gr.Interface(
+    fn=answer_question,
+    inputs=[gr.Image(type="pil"), gr.Textbox(placeholder="Enter your question")],
+    outputs=gr.Textbox(label="Answer"),
+    title="Visual Question Answering with BLIP",
+    description="Upload an image and ask a question about its content.",
+    examples=[("image1.jpeg", "Is there a man or a woman in the image?")],
+)
+
+iface.launch()
+