Create app.py

app.py

@@ -0,0 +1,94 @@
+import gradio as gr
+import requests
+from PIL import Image
+from io import BytesIO
+import numpy as np
+from landingai.common import decode_bitmap_rle
+import cv2
+import pydantic
+
+ENDPOINT_ID = "ba678fa4-65d1-4b87-8c85-cebd15224783"
+API_KEY = "land_sk_ikq7WEKGtaKI7pXIcKt2x7RoyYE6FBReqGOmKtEhjcmFbLbQsK"
+API_URL = f"https://predict.app.landing.ai/inference/v1/predict?endpoint_id={ENDPOINT_ID}"
+
+def predict_from_landinglens(image_path):
+    # Load and keep original image
+    original_img = Image.open(image_path).convert("RGB")
+    img_array = np.array(original_img)
+    
+    # Get image dimensions
+    height, width = img_array.shape[:2]
+    total_pixels = height * width
+    
+    # Prepare for API
+    buffered = BytesIO()
+    original_img.save(buffered, format="JPEG")
+    img_bytes = buffered.getvalue()
+
+    files = {"file": (image_path, img_bytes, "image/jpeg")}
+    headers = {"apikey": API_KEY}
+
+    try:
+        response = requests.post(API_URL, files=files, headers=headers)
+        if response.status_code == 503:
+            return "Service temporarily unavailable. Please try again later."
+        response.raise_for_status()
+        prediction = response.json()
+
+        if "predictions" not in prediction or not prediction.get("predictions"):
+            print("No 'predictions' key found or it's empty.")
+            return "Error: No 'predictions' found."
+
+        bitmaps = prediction["predictions"]["bitmaps"]
+        masked_images = []
+        coverage_info = []
+
+        for i, (bitmap_id, bitmap_data) in enumerate(bitmaps.items()):
+            try:
+                # Decode mask
+                mask = decode_bitmap_rle(bitmap_data["bitmap"])
+                if isinstance(mask, list):
+                    mask = np.array(mask)
+                
+                # Reshape mask to match image dimensions
+                mask = mask.reshape(prediction["predictions"]["imageHeight"], 
+                                  prediction["predictions"]["imageWidth"])
+                
+                # Calculate area coverage
+                mask_area = np.sum(mask > 0)
+                coverage_percentage = (mask_area / total_pixels) * 100
+                label_name = bitmap_data.get("label_name", f"Mask {i}")
+                coverage_info.append(f"{label_name}: {coverage_percentage:.2f}%")
+                
+                # Create colored overlay
+                colored_mask = np.zeros_like(img_array)
+                colored_mask[mask > 0] = [255, 0, 0]  # Red overlay for mask
+                
+                # Combine original image with colored mask
+                alpha = 0.5  # Transparency of the overlay
+                combined = cv2.addWeighted(img_array, 1, colored_mask, alpha, 0)
+                
+                # Convert to PIL Image
+                masked_image = Image.fromarray(combined)
+                masked_images.append(masked_image)
+
+            except Exception as e:
+                print(f"Error processing mask {i}: {e}")
+                continue
+
+        return masked_images, "\n".join(coverage_info)
+
+    except requests.exceptions.RequestException as e:
+        print(f"API Error: {e}")
+        return f"API Error: {e}"
+
+iface = gr.Interface(
+    fn=predict_from_landinglens,
+    inputs=gr.Image(type="filepath"),
+    outputs=[
+        gr.Gallery(format="png"),
+        gr.Textbox(label="Area of each mask in the image")
+    ],
+    title="Crosswalk detection model",
+)
+iface.launch()