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app.py

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+import os
+import gradio as gr
+import requests
+from PIL import Image
+from io import BytesIO
+from base64 import b64encode
+
+SEGMIND_MODEL_URL = "https://api.segmind.com/v1/inpaint-auto"
+
+def urlToB64(imgUrl):
+    return str(b64encode(requests.get(imgUrl).content))[2:-1]
+
+def imageToB64(img):
+    buffered = BytesIO()
+    img.save(buffered, format="JPEG")
+    return str(b64encode(buffered.getvalue()))[2:-1]
+
+def generate_image(
+    upload_method,
+    img_url,
+    uploaded_img,
+    prompt,
+    negative_prompt,
+    cn_model,
+    cn_processor,
+    base_model
+):
+    if upload_method == "URL":
+        if not img_url:
+            raise ValueError("Image URL is required.")
+        img_b64 = urlToB64(img_url)
+    else:
+        if not uploaded_img:
+            raise ValueError("Image upload is required.")
+        img_b64 = imageToB64(uploaded_img)
+
+    data = {
+        "image": img_b64,
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "samples": 1,
+        "base_model": base_model,
+        "cn_model": cn_model,
+        "cn_processor": cn_processor,
+        "scheduler": "DPM++ 2M SDE Karras",
+        "num_inference_steps": 25,
+        "guidance_scale": 7.5,
+        "seed": -1,
+        "strength": 0.9,
+        "base64": False,
+    }
+    response = requests.post(
+      SEGMIND_MODEL_URL,
+      json=data,
+      headers={"x-api-key": os.environ['SEGMIND_API_KEY']}
+    )
+    output_img = Image.open(BytesIO(response.content))
+
+    return output_img
+
+
+def invertBox(upload_method):
+    # Return gr.update objects with visibility settings
+    if upload_method == "URL":
+        return gr.update(visible=True), gr.update(visible=False)
+    else:
+        return gr.update(visible=False), gr.update(visible=True)
+
+with gr.Blocks() as demo:
+    gr.Markdown("### Photo Background Changer")
+    gr.Markdown(
+        "Change the bavkground of the image in one click to anything that you can imagine"
+    )
+    with gr.Row():
+        upload_method = gr.Radio(
+            choices=["URL", "Upload"], label="Choose Image Upload Method", value="URL"
+        )
+        img_url = gr.Textbox(label="Image URL")
+        uploaded_img = gr.Image(type="pil", label="Upload Image", visible=False)
+        upload_method.change(
+            invertBox, inputs=upload_method, outputs=[img_url, uploaded_img]
+        )
+    with gr.Row():
+        prompt = gr.Textbox(label="Prompt")
+        negative_prompt = gr.Textbox(
+            label="Negative Prompt",
+            value="disfigured, deformed, ugly, floating in air, blur, haze, uneven edges, improper blending, animated, cartoon",
+        )
+    with gr.Row():
+        cn_model = gr.Dropdown(
+            label="Select Controlnet Model",
+            choices=["Canny", "Depth", "SoftEdge", "OpenPose"],
+            value="Depth",
+        )
+        cn_processor = gr.Dropdown(
+            label="Select Controlnet Processor",
+            choices=[
+                "canny",
+                "depth",
+                "depth_leres",
+                "depth_leres++",
+                "hed",
+                "hed_safe",
+                "mediapipe_face",
+                "mlsd",
+                "normal_map",
+                "openpose",
+                "openpose_hand",
+                "openpose_face",
+                "openpose_faceonly",
+                "openpose_full",
+                "dw_openpose_full",
+                "animal_openpose",
+                "clip_vision",
+                "revision_clipvision",
+                "revision_ignore_prompt",
+                "ip-adapter_clip_sd15",
+                "ip-adapter_clip_sdxl_plus_vith",
+                "ip-adapter_clip_sdxl",
+                "color",
+                "pidinet",
+                "pidinet_safe",
+                "pidinet_sketch",
+                "pidinet_scribble",
+                "scribble_xdog",
+                "scribble_hed",
+                "segmentation",
+                "threshold",
+                "depth_zoe",
+                "normal_bae",
+                "oneformer_coco",
+                "oneformer_ade20k",
+                "lineart",
+                "lineart_coarse",
+                "lineart_anime",
+                "lineart_standard",
+                "shuffle",
+                "tile_resample",
+                "invert",
+                "lineart_anime_denoise",
+                "reference_only",
+                "reference_adain",
+                "reference_adain+attn",
+                "inpaint",
+                "inpaint_only",
+                "inpaint_only+lama",
+                "tile_colorfix",
+                "tile_colorfix+sharp",
+                "recolor_luminance",
+                "recolor_intensity",
+                "blur_gaussian",
+                "anime_face_segment",
+            ],
+            value="canny",
+        )
+    with gr.Row():
+        base_model = gr.Dropdown(
+            label="Select Base SD Model to use",
+            choices=["Real Vision XL", "SDXL", "Juggernaut XL", "DreamShaper XL"],
+            value="Juggernaut XL",
+        )
+    with gr.Row():
+        generate_btn = gr.Button("Generate Image")
+    output_image = gr.Image(type="pil")
+
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[
+            upload_method,
+            img_url,
+            uploaded_img,
+            prompt,
+            negative_prompt,
+            cn_model,
+            cn_processor,
+            base_model
+        ],
+        outputs=[output_image],
+    )
+
+demo.launch(debug=True)