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app.py

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+import cv2
+import numpy as np
+from PIL import Image
+import gradio as gr
+from transformers import SamModel, SamProcessor
+
+# Download model automatically (no .pth needed)
+processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+model = SamModel.from_pretrained("facebook/sam-vit-base")
+
+def auto_dimension(image, height, width):
+    image_np = np.array(image)
+
+    # Prepare for SAM
+    inputs = processor(image, input_points=None, return_tensors="pt")
+
+    # Run SAM
+    outputs = model(**inputs)
+    masks = processor.post_process_masks(
+        outputs.pred_masks,
+        inputs["original_sizes"],
+        inputs["reshaped_input_sizes"]
+    )
+    mask = masks[0][0].numpy().astype(np.uint8) * 255
+
+    # Find largest contour
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    cnt = max(contours, key=cv2.contourArea)
+    x, y, w, h = cv2.boundingRect(cnt)
+
+    # Scale using real height
+    scale = height / h
+    computed_width = w * scale
+
+    output = image_np.copy()
+
+    # Draw dimension lines
+    def draw_dim(pt1, pt2, text):
+        cv2.line(output, pt1, pt2, (0, 255, 0), 3)
+        cv2.putText(output, text, (pt1[0]+10, pt1[1]-10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
+
+    draw_dim((x-40, y), (x-40, y+h), f"H: {height} cm")
+    draw_dim((x, y+h+40), (x+w, y+h+40), f"W: {width} cm")
+
+    return Image.fromarray(output)
+
+iface = gr.Interface(
+    fn=auto_dimension,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Number(label="Height (cm)"),
+        gr.Number(label="Width (cm)")
+    ],
+    outputs=gr.Image(),
+    title="Automatic Dimension Overlay Tool",
+    description="Upload an image → enter real dimensions → AI adds dimension graphics automatically."
+)
+
+iface.launch()

requirements.txt

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+transformers
+torch
+opencv-python-headless
+numpy
+Pillow
+gradio