initial commit

app.py

@@ -1,94 +0,0 @@
-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()