upload app

app.py

@@ -0,0 +1,215 @@
+import os
+import gradio as gr
+import numpy as np
+import torch
+import random
+from PIL import Image, ImageDraw
+from typing import Iterable
+from gradio.themes import Soft
+from gradio.themes.utils import colors, fonts, sizes
+from transformers import Sam3Processor, Sam3Model
+
+# --- Handle optional 'spaces' import for local compatibility ---
+try:
+    import spaces
+except ImportError:
+    class spaces:
+        @staticmethod
+        def GPU(duration=60):
+            def decorator(func):
+                return func
+            return decorator
+
+# --- Custom Theme Setup (Plum) ---
+colors.plum = colors.Color(
+    name="plum",
+    c50="#FDF4FD",
+    c100="#F7E6F7",
+    c200="#ECD0EC",
+    c300="#DDA0DD",  # Plum
+    c400="#C98BC9",
+    c500="#B060B0",  
+    c600="#964B96",
+    c700="#7A3A7A",
+    c800="#602C60",
+    c900="#451E45",
+    c950="#2B122B",
+)
+
+class PlumTheme(Soft):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.plum,
+        secondary_hue: colors.Color | str = colors.plum,
+        neutral_hue: colors.Color | str = colors.slate,
+        text_size: sizes.Size | str = sizes.text_lg,
+        font: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
+        ),
+        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        self.set(
+            background_fill_primary="*primary_50",
+            background_fill_primary_dark="*primary_900",
+            body_background_fill="linear-gradient(135deg, *primary_100, *primary_50)",
+            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
+            button_primary_text_color="white",
+            button_primary_text_color_hover="white",
+            button_primary_background_fill="linear-gradient(90deg, *primary_500, *primary_600)",
+            button_primary_background_fill_hover="linear-gradient(90deg, *primary_600, *primary_700)",
+            button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *primary_800)",
+            button_primary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
+            button_secondary_text_color="black",
+            button_secondary_text_color_hover="white",
+            button_secondary_background_fill="linear-gradient(90deg, *primary_200, *primary_200)",
+            button_secondary_background_fill_hover="linear-gradient(90deg, *primary_400, *primary_400)",
+            button_secondary_background_fill_dark="linear-gradient(90deg, *primary_500, *primary_600)",
+            button_secondary_background_fill_hover_dark="linear-gradient(90deg, *primary_500, *primary_500)",
+            slider_color="*primary_500",
+            slider_color_dark="*primary_600",
+            block_title_text_weight="600",
+            block_border_width="3px",
+            block_shadow="*shadow_drop_lg",
+            button_primary_shadow="*shadow_drop_lg",
+            button_large_padding="11px",
+            color_accent_soft="*primary_100",
+            block_label_background_fill="*primary_200",
+        )
+
+plum_theme = PlumTheme()
+
+# --- Hardware Setup ---
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# --- Model Loading ---
+# Using the facebook/sam3 model as requested
+try:
+    print("Loading SAM3 Model and Processor...")
+    model = Sam3Model.from_pretrained("facebook/sam3").to(device)
+    processor = Sam3Processor.from_pretrained("facebook/sam3")
+    print("Model loaded successfully.")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    print("Ensure you have the correct libraries installed and access to the model.")
+    # Fallback/Placeholder for demonstration if model doesn't exist in environment yet
+    model = None 
+    processor = None
+
+@spaces.GPU(duration=60)
+def segment_image(input_image, text_prompt, threshold=0.5):
+    if input_image is None:
+        raise gr.Error("Please upload an image.")
+    if not text_prompt:
+        raise gr.Error("Please enter a text prompt (e.g., 'cat', 'face').")
+    
+    if model is None or processor is None:
+        raise gr.Error("Model not loaded correctly.")
+
+    # Convert image to RGB
+    image_pil = input_image.convert("RGB")
+
+    # Preprocess
+    inputs = processor(images=image_pil, text=text_prompt, return_tensors="pt").to(device)
+
+    # Inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Post-process results
+    results = processor.post_process_instance_segmentation(
+        outputs,
+        threshold=threshold,
+        mask_threshold=0.5,
+        target_sizes=inputs.get("original_sizes").tolist()
+    )[0]
+
+    masks = results['masks'] # Boolean tensor [N, H, W]
+    scores = results['scores']
+    
+    # Prepare for Gradio AnnotatedImage
+    # Gradio expects (image, [(mask, label), ...])
+    
+    annotations = []
+    masks_np = masks.cpu().numpy()
+    scores_np = scores.cpu().numpy()
+    
+    for i, mask in enumerate(masks_np):
+        # mask is a boolean array (True/False). 
+        # AnnotatedImage handles the coloring automatically.
+        # We just pass the mask and a label.
+        score_val = scores_np[i]
+        label = f"{text_prompt} ({score_val:.2f})"
+        annotations.append((mask, label))
+    
+    # Return tuple format for AnnotatedImage
+    return (image_pil, annotations)
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 980px;
+}
+#main-title h1 {font-size: 2.1em !important;}
+"""
+
+with gr.Blocks(css=css, theme=plum_theme) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(
+            "# **SAM3 Image Segmentation**", 
+            elem_id="main-title"
+        )
+        
+        gr.Markdown("Segment objects in images using **SAM3** (Segment Anything Model 3) with text prompts.")
+
+        with gr.Row():
+            # Left Column: Inputs
+            with gr.Column(scale=1):
+                input_image = gr.Image(label="Input Image", type="pil", height=350)
+                text_prompt = gr.Textbox(
+                    label="Text Prompt",
+                    placeholder="e.g., cat, ear, car wheel...",
+                    info="What do you want to segment?"
+                )
+                threshold = gr.Slider(label="Confidence Threshold", minimum=0.0, maximum=1.0, value=0.4, step=0.05)
+                
+                run_button = gr.Button("Segment", variant="primary")
+
+            # Right Column: Output
+            with gr.Column(scale=1.5):
+                # AnnotatedImage creates a nice overlay visualization
+                output_image = gr.AnnotatedImage(label="Segmented Output", height=500)
+
+        # Examples
+        gr.Examples(
+            examples=[
+                ["examples/cat.jpg", "cat", 0.5],
+                ["examples/car.jpg", "tire", 0.4],
+                ["examples/fruit.jpg", "apple", 0.5],
+            ],
+            inputs=[input_image, text_prompt, threshold],
+            outputs=[output_image],
+            fn=segment_image,
+            cache_examples=False,
+            label="Examples (Ensure files exist in 'examples/' folder)"
+        )
+
+    run_button.click(
+        fn=segment_image,
+        inputs=[input_image, text_prompt, threshold],
+        outputs=[output_image]
+    )
+
+if __name__ == "__main__":
+    demo.launch(ssr_mode=False, show_error=True)