Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
+import spaces
+import torch
+
+model = PaliGemmaForConditionalGeneration.from_pretrained("gokaygokay/sd3-long-captioner", device="cuda").eval()
+processor = PaliGemmaProcessor.from_pretrained("gokaygokay/sd3-long-captioner")
+
+@spaces.GPU
+def create_captions_rich(image):   
+    prompt = "caption en"
+    model_inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
+    input_len = model_inputs["input_ids"].shape[-1]
+
+    with torch.inference_mode():
+        generation = model.generate(**model_inputs, max_new_tokens=256, do_sample=False)
+        generation = generation[0][input_len:]
+        decoded = processor.decode(generation, skip_special_tokens=True)
+    return decoded
+
+css = """
+  #mkd {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+  gr.HTML("<h1><center>PaliGemma Fine-tuned for Long Captioning for Stable Diffusion 3.<center><h1>")
+  with gr.Tab(label="PaliGemma Rich Captions"):
+    with gr.Row():
+      with gr.Column():
+        input_img = gr.Image(label="Input Picture")
+        submit_btn = gr.Button(value="Submit")
+      output = gr.Text(label="Caption")
+
+    gr.Examples(
+    [["assets/image1.png"], ["assets/image2.PNG"], ["assets/image3.jpg"]],
+    inputs = [input_img],
+    outputs = [output],
+    fn=create_captions_rich,
+    label='Try captioning on examples'
+    )
+
+    submit_btn.click(create_captions_rich, [input_img], [output])
+
+demo.launch(debug=True)