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app.py

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+import os
+import torch
+import gradio as gr
+from PIL import Image
+from transformers import AutoModelForCausalLM,AutoProcessor
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+processor = AutoProcessor.from_pretrained("microsoft/git-base")
+model = AutoModelForCausalLM.from_pretrained("sam749/sd-portrait-caption").to(device)
+
+def generate_captions(images:[Image],max_length=200):
+  # prepare image for the model
+  inputs = processor(images=images, return_tensors="pt").to(device)
+  pixel_values = inputs.pixel_values
+  generated_ids = model.generate(pixel_values=pixel_values, max_length=max_length)
+  generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)
+  return generated_caption
+
+def generate_caption(image,max_length=200):
+  return generate_captions(image,max_length)[0]
+
+
+inputs = [
+    gr.Image(sources=["upload", "clipboard"],
+             height=400,
+             type="pil"
+             ),
+    gr.Slider(minimum=10,
+              maximum=400,
+              value=200,
+              label='max length',
+              step=8,
+              )
+]
+outputs = [
+    gr.Text(label="Generated Caption"),
+]
+
+demo = gr.Interface(
+    fn=generate_caption,
+    inputs=inputs,
+    outputs=outputs,
+    title="Stable Diffusion Portrait Captioner",
+    theme="gradio/monochrome",
+    api_name="caption",
+    submit_btn=gr.Button("caption it", variant="primary"),
+    allow_flagging="never",
+)
+demo.queue(
+    max_size=10,
+)
+
+demo.launch()

requirements.txt

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+torch
+gradio
+pillow
+transformers