Update app.py

app.py

@@ -9,6 +9,7 @@ import shutil
 import base64
 import pytz
 import time
+from PIL import ImageEnhance
 
 # Load environment variables
 print("Loading environment variables at", datetime.now(pytz.timezone("Asia/Kolkata")).strftime("%Y-%m-%d %H:%M:%S IST"))
@@ -51,16 +52,35 @@ milestone_percentage_map = {
 # Adjust the timezone to your local timezone
 local_timezone = pytz.timezone("Asia/Kolkata")
 
-# Enhanced AI model prediction with heuristic for completion
+# Enhanced AI model prediction with stricter heuristic for completion
 def mock_ai_model(image):
     img = image.convert("RGB")
     max_size = 1024
     img.thumbnail((max_size, max_size), Image.Resampling.LANCZOS)
 
-    # Simple heuristic for Final Completion (100%)
-    img_data = list(img.getdata())
-    color_variation = max(max(pixel) - min(pixel) for pixel in img_data)  # Measure color uniformity
-    if color_variation < 20 and all(sum(pixel) / 3 > 200 for pixel in img_data[:1000]):  # Bright and uniform
+    # Enhance contrast to detect features
+    enhancer = ImageEnhance.Contrast(img)
+    img_enhanced = enhancer.enhance(2.0)
+
+    # Stricter heuristic for Final Completion (100%)
+    img_data = list(img_enhanced.getdata())
+    total_pixels = len(img_data)
+    brightness_avg = sum(sum(pixel) / 3 for pixel in img_data) / total_pixels
+    color_variation = max(max(pixel) - min(pixel) for pixel in img_data)
+
+    # Edge detection (simple count of significant color changes)
+    edge_count = 0
+    width, height = img_enhanced.size
+    for x in range(width - 1):
+        for y in range(height - 1):
+            r, g, b = img_enhanced.getpixel((x, y))
+            r_next, g_next, b_next = img_enhanced.getpixel((x + 1, y + 1))
+            if abs(r - r_next) > 50 or abs(g - g_next) > 50 or abs(b - b_next) > 50:
+                edge_count += 1
+    edge_ratio = edge_count / (width * height)
+
+    # Final Completion criteria: high brightness, low variation, low edges (finished look)
+    if brightness_avg > 220 and color_variation < 15 and edge_ratio < 0.05:
         return "Final Completion", 100, 0.95
 
     # Fallback to hash-based simulation