Create app.py

app.py

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+!pip install git+https://github.com/facebookresearch/segment-anything.git
+!pip install opencv-python pillow matplotlib
+!pip uninstall -y diffusers transformers
+!pip install diffusers==0.30.3 transformers==4.44.2 accelerate
+!pip install gradio
+
+import gradio as gr
+import numpy as np
+import torch
+from PIL import Image, ImageDraw
+import cv2
+from segment_anything import sam_model_registry, SamPredictor
+from diffusers import StableDiffusionXLInpaintPipeline
+import os
+
+# ============================================================
+# SAM SMART FOREGROUND SELECTOR - OFFLINE MODEL VERSION
+# ============================================================
+
+# Global state
+global_state = {
+    "sam_predictor": None,
+    "original_image": None,
+    "current_mask": None,
+    "rgba_image": None,
+    "image_set": False,
+    "box_points": [],
+    "positive_points": [],
+    "negative_points": [],
+    "auto_masks": []
+}
+
+# ------------------------------------------------------------
+# 🧩 Load SAM Model (Offline from /models/)
+# ------------------------------------------------------------
+def load_sam_model(sam_checkpoint="models/sam_vit_b_01ec64.pth"):
+    """Load SAM model from local folder models/"""
+    if global_state["sam_predictor"] is None:
+        if not os.path.exists(sam_checkpoint):
+            raise FileNotFoundError(
+                f"❌ File model tidak ditemukan: {sam_checkpoint}\n"
+                f"Pastikan kamu sudah upload 'sam_vit_b_01ec64.pth' ke folder /models/"
+            )
+
+        print(f"✅ Loading SAM model from {sam_checkpoint}...")
+        sam = sam_model_registry["vit_b"](checkpoint=sam_checkpoint)
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        sam.to(device=device)
+        predictor = SamPredictor(sam)
+        global_state["sam_predictor"] = predictor
+        print(f"✅ SAM loaded successfully on {device}")
+    return global_state["sam_predictor"]
+
+# ------------------------------------------------------------
+# 🖼️ Step 1: Upload Image
+# ------------------------------------------------------------
+def process_upload(image):
+    if image is None:
+        return None, "⚠️ Upload gambar terlebih dahulu!", None, None
+
+    image_np = np.array(image)
+    global_state["original_image"] = image_np.copy()
+    predictor = load_sam_model()
+    predictor.set_image(image_np)
+    global_state["image_set"] = True
+
+    status = "✅ Gambar berhasil diupload!\n\nKlik 'Auto Smart Select' atau gunakan Box/Point untuk memilih objek."
+    return Image.fromarray(image_np), status, None, None
+
+# ------------------------------------------------------------
+# ✨ Step 2: Auto Smart Select (Pilih Objek Otomatis)
+# ------------------------------------------------------------
+def auto_smart_select():
+    if not global_state["image_set"]:
+        return None, None, "⚠️ Upload gambar terlebih dahulu!"
+
+    from segment_anything import SamAutomaticMaskGenerator
+
+    predictor = global_state["sam_predictor"]
+    sam_model = predictor.model
+
+    mask_generator = SamAutomaticMaskGenerator(
+        model=sam_model,
+        points_per_side=32,
+        pred_iou_thresh=0.88,
+        stability_score_thresh=0.93,
+        min_mask_region_area=500
+    )
+
+    masks = mask_generator.generate(global_state["original_image"])
+    if not masks:
+        return None, None, "❌ Tidak ada objek terdeteksi."
+
+    # Pilih mask terbaik berdasarkan area dan posisi tengah
+    h, w = global_state["original_image"].shape[:2]
+    center_x, center_y = w // 2, h // 2
+
+    def score_mask(mask_data):
+        mask = mask_data['segmentation']
+        y, x = np.where(mask)
+        if len(x) == 0: return 0
+        dist = np.sqrt((x.mean() - center_x)**2 + (y.mean() - center_y)**2)
+        dist_score = 1 - (dist / np.sqrt(center_x**2 + center_y**2))
+        area_ratio = mask_data['area'] / (h * w)
+        size_score = 1 - abs(area_ratio - 0.15)  # ideal sekitar 15%
+        return dist_score * 0.6 + size_score * 0.4
+
+    best_mask = max(masks, key=score_mask)['segmentation']
+    rgba_image = np.dstack((global_state["original_image"], (best_mask * 255).astype(np.uint8)))
+    global_state["current_mask"] = best_mask
+    global_state["rgba_image"] = rgba_image
+
+    segmented_preview = Image.fromarray(rgba_image)
+    mask_preview = Image.fromarray((best_mask * 255).astype(np.uint8))
+
+    status = "✅ Foreground otomatis terpilih!\nKlik 'Generate Background' untuk mengganti latar belakang."
+    return segmented_preview, mask_preview, status
+
+# ------------------------------------------------------------
+# 🎨 Step 3: Ganti Background dengan Prompt
+# ------------------------------------------------------------
+def change_background(background_prompt, negative_prompt, guidance_scale, num_steps, seed):
+    if global_state["rgba_image"] is None:
+        return None, "⚠️ Generate mask dulu!"
+
+    rgba_image = Image.fromarray(global_state["rgba_image"])
+    pipeline = StableDiffusionXLInpaintPipeline.from_pretrained(
+        "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+        torch_dtype=torch.float16,
+        variant="fp16"
+    )
+    if torch.cuda.is_available():
+        pipeline = pipeline.to("cuda")
+
+    alpha = np.array(rgba_image.split()[-1])
+    mask = np.where(alpha < 128, 255, 0).astype(np.uint8)
+    mask = Image.fromarray(mask).convert("L")
+    rgb_image = rgba_image.convert("RGB")
+
+    generator = torch.manual_seed(int(seed))
+    result = pipeline(
+        image=rgb_image,
+        mask_image=mask,
+        prompt=background_prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        generator=generator,
+        num_inference_steps=int(num_steps),
+        width=rgb_image.width,
+        height=rgb_image.height
+    ).images[0]
+
+    status = f"✅ Background diganti: {background_prompt}"
+    return result, status
+
+# ------------------------------------------------------------
+# 🧱 Gradio UI
+# ------------------------------------------------------------
+def create_gradio_interface():
+    with gr.Blocks(title="AI Background Maker (Offline SAM)") as demo:
+        gr.Markdown("# 🖼️ AI Background Maker\nUpload foto → Pilih objek → Ganti background ✨")
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                input_image = gr.Image(label="Upload Gambar", type="pil")
+                upload_btn = gr.Button("📥 Load Image", variant="primary")
+                auto_smart_btn = gr.Button("🤖 Auto Smart Select (No Click!)", variant="primary")
+                bg_prompt = gr.Textbox(value="sunset beach, cinematic, 8k", label="Background Prompt")
+                neg_prompt = gr.Textbox(value="blurry, low quality", label="Negative Prompt")
+                guidance = gr.Slider(1, 15, 7, 0.5, label="Guidance Scale")
+                steps = gr.Slider(10, 50, 30, 5, label="Steps")
+                seed = gr.Number(value=42, label="Seed")
+                gen_bg = gr.Button("🚀 Generate Background", variant="secondary")
+
+            with gr.Column(scale=1):
+                status = gr.Textbox(label="Status", lines=5)
+                segmented = gr.Image(label="Foreground")
+                mask = gr.Image(label="Mask")
+                result = gr.Image(label="Final Output")
+                result_status = gr.Textbox(label="Output Status", lines=2)
+
+        upload_btn.click(process_upload, inputs=[input_image],
+                         outputs=[segmented, status, mask, result])
+        auto_smart_btn.click(auto_smart_select, outputs=[segmented, mask, status])
+        gen_bg.click(change_background,
+                     inputs=[bg_prompt, neg_prompt, guidance, steps, seed],
+                     outputs=[result, result_status])
+    return demo
+
+if __name__ == "__main__":
+    demo = create_gradio_interface()
+    demo.launch(share=True, debug=True, server_name="0.0.0.0", server_port=7860)