Create app.py

app.py

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+import torch
+from PIL import Image
+import gradio as gr
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+model = torch.hub.load('mair-lab/mapl-private', 'mapl')
+model.eval()
+model.to(device)
+
+
+def predict(image: Image.Image, question: str) -> str:
+    pixel_values = model.image_transform(image).unsqueeze(0).to(device)
+
+    input_ids = None
+    if question:
+        text = f"Please answer the question. Question: {question} Answer:" if '?' in question else question
+        input_ids = model.text_transform(text).input_ids.to(device)
+    
+    with torch.autocast(device_type=device, dtype=torch.float16):
+        generated_ids = model.generate(
+            pixel_values=pixel_values,
+            input_ids=input_ids,
+            max_new_tokens=50,
+            num_beams=5
+        )
+    
+    answer = model.text_processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+
+    return answer
+
+
+image = gr.components.Image(type='pil')
+question = gr.components.Textbox(value="What is this?", label="Question")
+answer = gr.components.Textbox(label="Answer")
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=[image, question],
+    outputs=answer,
+    allow_flagging='never')
+interface.launch()