Update app.py

app.py

@@ -8,20 +8,15 @@ from PIL import Image, ImageDraw
 import gradio as gr
 
 # ────────── Secrets & endpoints ──────────
-BASETEN_MODEL_URL = "https://app.baseten.co/models/YOUR_MODEL_ID/predict" 
+BASETEN_MODEL_URL = "https://app.baseten.co/models/YOUR_MODEL_ID/predict"
 BASETEN_API_KEY = os.getenv("BASETEN_API_KEY")
 REPLICATE_TOKEN = os.getenv("REPLICATE_API_TOKEN")
 
-from florence_sam.detect_and_segment import fill_detected_bboxes
-
 # ────────── Globals ──────────
-ADAPTER_NAME = "inpaint"
 ADAPTER_SIZE = 1024
-model_config = dict(union_cond_attn=True, add_cond_attn=False,
-                    latent_lora=False, independent_condition=False)
 css = "#col-container {margin:0 auto; max-width:960px;}"
 
-#Background prompt via Replicate 
+# Background generation via Replicate
 def _gen_bg(prompt: str):
     url = replicate.run(
         "google/imagen-4-fast",
@@ -30,48 +25,43 @@ def _gen_bg(prompt: str):
     url = url[0] if isinstance(url, list) else url
     return Image.open(BytesIO(requests.get(url, timeout=120).content)).convert("RGB")
 
-#@spaces.GPU
-def process_image_and_text(subject_image, adapter_dict, prompt, use_detect, detect_prompt, size=ADAPTER_SIZE, rank=10.0):
+# Main processing function
+def process_image_and_text(subject_image, adapter_dict, prompt, _unused1, _unused2, size=ADAPTER_SIZE, rank=10.0):
     seed, guidance_scale, steps = 42, 2.5, 28
 
-    if use_detect:
-        base_img = adapter_dict["image"] if isinstance(adapter_dict, dict) else adapter_dict
-        if base_img is None:
-            raise gr.Error("Upload a background image first.")
-        adapter_image, _ = fill_detected_bboxes(
-            image=base_img, text=detect_prompt,
-            inflate_pct=0.15, fill_color="#00FF00"
-        )
-    else:
-        adapter_image = adapter_dict["image"] if isinstance(adapter_dict, dict) else adapter_dict
-        if isinstance(adapter_dict, dict) and adapter_dict.get("mask") is not None:
-            m = adapter_dict["mask"].convert("L").point(lambda p: 255 if p else 0)
-            if bbox := m.getbbox():
-                rect = Image.new("L", m.size, 0)
-                ImageDraw.Draw(rect).rectangle(bbox, fill=255)
-                m = rect
-            green = Image.new("RGB", adapter_image.size, "#00FF00")
-            adapter_image = Image.composite(green, adapter_image, m)
+    adapter_image = adapter_dict["image"] if isinstance(adapter_dict, dict) else adapter_dict
+    if isinstance(adapter_dict, dict) and adapter_dict.get("mask") is not None:
+        m = adapter_dict["mask"].convert("L").point(lambda p: 255 if p else 0)
+        if bbox := m.getbbox():
+            rect = Image.new("L", m.size, 0)
+            ImageDraw.Draw(rect).rectangle(bbox, fill=255)
+            m = rect
+        green = Image.new("RGB", adapter_image.size, "#00FF00")
+        adapter_image = Image.composite(green, adapter_image, m)
 
     def prep(img: Image.Image):
         w, h = img.size
         m = min(w, h)
-        return img.crop(((w-m)//2, (h-m)//2, (w+m)//2, (h+m)//2)).resize((size, size), Image.LANCZOS)
+        return img.crop(((w - m) // 2, (h - m) // 2, (w + m) // 2, (h + m) // 2)).resize((size, size), Image.LANCZOS)
 
     subj_proc = prep(subject_image)
     adap_proc = prep(adapter_image)
 
     def b64(img):
-        buf = BytesIO(); img.save(buf, format="PNG")
+        buf = BytesIO()
+        img.save(buf, format="PNG")
         return base64.b64encode(buf.getvalue()).decode()
 
     payload = {
         "prompt": prompt,
         "subject_image": b64(subj_proc),
         "adapter_image": b64(adap_proc),
-        "height": size, "width": size,
-        "steps": steps, "seed": seed,
-        "guidance_scale": guidance_scale, "rank": rank,
+        "height": size,
+        "width": size,
+        "steps": steps,
+        "seed": seed,
+        "guidance_scale": guidance_scale,
+        "rank": rank,
     }
 
     headers = {"Content-Type": "application/json"}
@@ -121,53 +111,30 @@ with gr.Blocks(css=css, title="ZenCtrl Inpainting") as demo:
     with gr.Row():
         with gr.Column(scale=2, elem_id="col-container"):
             subj_img = gr.Image(type="pil", label="Subject image")
+            ref_img = gr.Image(type="pil", label="Background / Mask image", tool="sketch", brush_color="#00FF00")
+            ref_img_ex = gr.Image(type="pil", visible=False)
 
-            # ⚑ Visible sketch component (what the user draws on)
-            ref_img  = gr.Image(
-                type="pil", label="Background / Mask image",
-                tool="sketch", brush_color="#00FF00"
-            )
-
-            # ⚑ Hidden plain-image component used ONLY for examples
-            ref_img_ex = gr.Image(
-                type="pil", visible=False
-            )
-
-            use_detect_ck = gr.Checkbox(False, label="Detect with Florence-SAM")
-            detect_box    = gr.Textbox(label="Detection prompt", value="person, chair", visible=False)
-            promptbox     = gr.Textbox(label="Generation prompt", value="furniture", lines=2)
-            run_btn       = gr.Button("Generate", variant="primary")
+            # Removed Florence-SAM
+            promptbox = gr.Textbox(label="Generation prompt", value="furniture", lines=2)
+            run_btn = gr.Button("Generate", variant="primary")
 
             with gr.Accordion("Advanced Settings", open=False):
                 bgprompt = gr.Textbox(label="Background Prompt", value="Scandinavian living room …")
-                bg_btn   = gr.Button("Generate BG")
+                bg_btn = gr.Button("Generate BG")
 
         with gr.Column(scale=2):
             gallery = gr.Gallery(columns=[1], rows=[1], object_fit="contain", height="auto")
-            bg_img  = gr.Image(label="Background", visible=False)
+            bg_img = gr.Image(label="Background", visible=False)
 
     # ---------- Example wrapper ---------------------------------
-    #from PIL import Image
     def _run_example(subj, bg, prompt):
-        # 1️⃣  Ensure we have PIL.Image objects
         if isinstance(subj, str):
             subj = Image.open(subj)
         if isinstance(bg, str):
             bg = Image.open(bg)
-    
-        # 2️⃣  Wrap background for pipeline (no mask)
+
         adapter_dict = {"image": bg, "mask": None}
-    
-        # 3️⃣  Call your real pipeline
-        gallery_out, _ = process_image_and_text(
-            subj,
-            adapter_dict,
-            prompt,
-            use_detect=False,
-            detect_prompt=""
-        )
-    
-        # 4️⃣  Also show the bg in the visible sketch component so user can draw
+        gallery_out, _ = process_image_and_text(subj, adapter_dict, prompt, False, "")
         return gallery_out, gr.update(value=bg)
 
     # ---------- Examples ----------------------------------------
@@ -178,8 +145,8 @@ with gr.Blocks(css=css, title="ZenCtrl Inpainting") as demo:
             ["examples/subject1.png", "examples/subject1.png", "Make this monster ride a skateboard on the beach"],
             ["examples/subject1.png", "examples/subject1.png", "Make this cat happy"],
         ],
-        inputs=[subj_img, ref_img_ex, promptbox],   # <-- uses the hidden plain image
-        outputs=[gallery, ref_img],                 # update gallery + visible sketch img
+        inputs=[subj_img, ref_img_ex, promptbox],
+        outputs=[gallery, ref_img],
         fn=_run_example,
         examples_per_page="all",
         label="Presets (Input · Background · Prompt)",
@@ -189,19 +156,11 @@ with gr.Blocks(css=css, title="ZenCtrl Inpainting") as demo:
     # ---------- Buttons & interactions --------------------------
     run_btn.click(
         process_image_and_text,
-        inputs=[subj_img, ref_img, promptbox, use_detect_ck, detect_box],
+        inputs=[subj_img, ref_img, promptbox, gr.State(False), gr.State("")],
         outputs=[gallery, raw_state]
     )
     bg_btn.click(_gen_bg, inputs=[bgprompt], outputs=[bg_img])
-    use_detect_ck.change(
-        lambda v: gr.update(visible=v), inputs=use_detect_ck, outputs=detect_box
-    )
 
 # ---------------- Launch ---------------------------------------
-
 if __name__ == "__main__":
-    #init_pipeline()
-    demo.launch(
-        debug=True,
-        share=True
-    )
+    demo.launch(debug=True, share=True)