Switch Space to Gradio UI: add Gradio app, update README and requirements

README.md

@@ -1,14 +1,13 @@
 ---
-title: Remove Photo Object
-emoji: ⚡
-colorFrom: pink
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.10.0
-python_version: 3.9.5
+title: Object Remover
+emoji: 🌍
+colorFrom: green
+colorTo: green
+sdk: gradio
+sdk_version: "5.47.0"
 app_file: app.py
 pinned: false
 license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -4,6 +4,7 @@
 import numpy as np
 import pandas as pd
 import streamlit as st
+import gradio as gr
 import os
 from datetime import datetime
 from PIL import Image
@@ -44,19 +45,21 @@ if 'reuse_image' not in st.session_state:
 def set_image(img):
     st.session_state.reuse_image = img
 
-st.title("AI Photo Object Removal")
 
-st.image(open("assets/demo.png", "rb").read())
+def run_streamlit_ui():
+    st.title("AI Photo Object Removal")
 
-st.markdown(
-    """
-    So you want to remove an object in your photo? You don't need to learn photo editing skills.
-    **Just draw over the parts of the image you want to remove, then our AI will remove them.**
-    """
-)
-uploaded_file = st.file_uploader("Choose image", accept_multiple_files=False, type=["png", "jpg", "jpeg"])
+    st.image(open("assets/demo.png", "rb").read())
 
-if uploaded_file is not None:
+    st.markdown(
+        """
+        So you want to remove an object in your photo? You don't need to learn photo editing skills.
+        **Just draw over the parts of the image you want to remove, then our AI will remove them.**
+        """
+    )
+    uploaded_file = st.file_uploader("Choose image", accept_multiple_files=False, type=["png", "jpg", "jpeg"])
+
+    if uploaded_file is not None:
     
     if st.session_state.reuse_image is not None:
         img_input = Image.fromarray(st.session_state.reuse_image)
@@ -114,8 +117,6 @@ if uploaded_file is not None:
         im[background]=[0,0,0,255]
         im[drawing]=[0,0,0,0] # RGBA
         
-        reuse = False
-        
         if st.button('Submit'):
             
             with st.spinner("AI is doing the magic!"):
@@ -136,3 +137,51 @@ if uploaded_file is not None:
             
             st.info("**TIP**: If the result is not perfect, you can download it then "
                     "upload then remove the artifacts.")
+
+
+def _prepare_mask_rgba(image: Image.Image, mask: np.ndarray) -> np.ndarray:
+    if mask is None:
+        rgba = np.zeros((image.height, image.width, 4), dtype=np.uint8)
+        rgba[:, :, 3] = 255
+        return rgba
+    mask_img = Image.fromarray(mask).convert("L").resize(image.size)
+    mask_np = np.array(mask_img)
+    rgba = np.zeros((image.height, image.width, 4), dtype=np.uint8)
+    rgba[:, :, :3] = 0
+    rgba[:, :, 3] = 255
+    rgba[mask_np > 0, 3] = 0
+    return rgba
+
+
+def gradio_inpaint(data):
+    if data is None:
+        return None
+    img = data.get("image") if isinstance(data, dict) else data
+    mask = data.get("mask") if isinstance(data, dict) else None
+    if img is None:
+        return None
+    if not isinstance(img, Image.Image):
+        img = Image.fromarray(img)
+    img_rgba = img.convert("RGBA")
+    rgba_mask = _prepare_mask_rgba(img_rgba, mask)
+    output = process_inpaint(np.array(img_rgba), rgba_mask)
+    return Image.fromarray(output).convert("RGB")
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("# AI Photo Object Removal")
+    gr.Markdown("Draw on the image to remove objects. Use the Sketch tool to paint the mask.")
+    with gr.Row():
+        input_image = gr.Image(label="Image", type="pil", tool="sketch", image_mode="RGBA", sources=["upload"]) 
+    run_btn = gr.Button("Remove Object")
+    output_image = gr.Image(label="Result", type="pil")
+    run_btn.click(gradio_inpaint, inputs=input_image, outputs=output_image)
+
+
+if __name__ == "__main__":
+    # Default to Gradio for HF Spaces
+    use_gradio = True
+    if use_gradio:
+        demo.queue().launch()
+    else:
+        run_streamlit_ui()

requirements.txt

@@ -4,6 +4,7 @@ numpy
 opencv-python-headless
 matplotlib
 streamlit
+gradio>=4.0.0
 streamlit-drawable-canvas==0.9.0
 pyyaml
 tqdm