Update app.py

app.py

@@ -126,44 +126,7 @@ class FloorPlanProcessor:
             logger.error(f"Image error: {str(e)}")
             pdf.error = str(e)
             return False
-
-    def _preprocess_floor_plan(self, image):
-    """
-    Enhance floor plan image for better analysis
-    """
-    try:
-        logger.info(f"Preprocessing image: {image.size}")
-        
-        # Convert to grayscale
-        if image.mode != 'L':
-            gray = image.convert('L')
-        else:
-            gray = image
-        
-        # Enhance contrast
-        enhancer = ImageEnhance.Contrast(gray)
-        gray = enhancer.enhance(1.5)
-        logger.info("✓ Contrast enhanced")
-        
-        # Sharpen
-        gray = gray.filter(ImageFilter.SHARPEN)
-        logger.info("✓ Sharpened")
-        
-        # Remove noise
-        gray = gray.filter(ImageFilter.MedianFilter(size=3))
-        logger.info("✓ Noise removed")
-        
-        # Convert back to RGB
-        result = gray.convert('RGB')
-        logger.info(f"✓ Preprocessing complete: {result.size}")
-        
-        return result
-        
-    except Exception as e:
-        logger.error(f"Preprocessing error: {str(e)}")
-        return image
-
-
+    
     async def extract_images_from_pdf(self, pdf, file_content):
         try:
             pdf_document = fitz.open(stream=file_content, filetype="pdf")
@@ -195,6 +158,74 @@ class FloorPlanProcessor:
             pdf.error = str(e)
             return False
     
+    def _select_single_best_image(self, images):
+        """Select the single best image"""
+        if len(images) == 1:
+            return images[0]
+        
+        # Score by area (largest = best for floor plans)
+        scored = [(img.size[0] * img.size[1], img) for img in images]
+        scored.sort(reverse=True, key=lambda x: x[0])
+        
+        best = scored[0][1]
+        logger.info(f"Selected best from {len(images)} images")
+        return best
+    
+    def _preprocess_floor_plan(self, image):
+        """
+        Enhance floor plan image for better analysis
+        - Improves contrast (helps model see room boundaries better)
+        - Sharpens edges (makes text/dimensions clearer)
+        - Removes noise (reduces confusion from scan artifacts)
+        """
+        try:
+            logger.info(f"Preprocessing image: {image.size}")
+            
+            # Step 1: Convert to grayscale for processing
+            if image.mode != 'L':
+                gray = image.convert('L')
+            else:
+                gray = image
+            
+            # Step 2: Enhance contrast (1.5x = moderate boost)
+            enhancer = ImageEnhance.Contrast(gray)
+            gray = enhancer.enhance(1.5)
+            logger.info("✓ Contrast enhanced")
+            
+            # Step 3: Sharpen to make text/lines clearer
+            gray = gray.filter(ImageFilter.SHARPEN)
+            logger.info("✓ Sharpened")
+            
+            # Step 4: Remove noise with median filter
+            gray = gray.filter(ImageFilter.MedianFilter(size=3))
+            logger.info("✓ Noise removed")
+            
+            # Step 5: Convert back to RGB for Gemini
+            result = gray.convert('RGB')
+            logger.info(f"✓ Preprocessing complete: {result.size}")
+            
+            return result
+            
+        except Exception as e:
+            logger.error(f"Preprocessing error: {str(e)}")
+            return image  # Return original if preprocessing fails
+    
+    def _optimize_image(self, image, target_size=2048):
+        """Optimize image for analysis"""
+        if image.mode not in ('RGB', 'L'):
+            image = image.convert('RGB')
+        
+        width, height = image.size
+        
+        if width > target_size or height > target_size:
+            ratio = target_size / max(width, height)
+            new_width = int(width * ratio)
+            new_height = int(height * ratio)
+            image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+            logger.info(f"Resized: {width}x{height} → {new_width}x{new_height}")
+        
+        return image
+    
     async def analyze_floor_plan(self, pdf_id, description=None):
         pdf = self.pdfs.get(pdf_id)
         if not pdf:
@@ -211,7 +242,6 @@ class FloorPlanProcessor:
         # Use ONLY the first/best image for single file analysis
         best_image = self._select_single_best_image(pdf.images)
         best_image = self._preprocess_floor_plan(best_image)
-    
         optimized_image = self._optimize_image(best_image, target_size=2048)
             
         logger.info(f"Using single image: {optimized_image.size[0]}x{optimized_image.size[1]}px")
@@ -259,35 +289,6 @@ class FloorPlanProcessor:
         logger.warning("All attempts failed, using fallback")
         return self._generate_fallback(pdf.measurement_info)
     
-    def _select_single_best_image(self, images):
-        """Select the single best image"""
-        if len(images) == 1:
-            return images[0]
-        
-        # Score by area (largest = best for floor plans)
-        scored = [(img.size[0] * img.size[1], img) for img in images]
-        scored.sort(reverse=True, key=lambda x: x[0])
-        
-        best = scored[0][1]
-        logger.info(f"Selected best from {len(images)} images")
-        return best
-    
-    def _optimize_image(self, image, target_size=2048):
-        """Optimize image for analysis"""
-        if image.mode not in ('RGB', 'L'):
-            image = image.convert('RGB')
-        
-        width, height = image.size
-        
-        if width > target_size or height > target_size:
-            ratio = target_size / max(width, height)
-            new_width = int(width * ratio)
-            new_height = int(height * ratio)
-            image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
-            logger.info(f"Resized: {width}x{height} → {new_width}x{new_height}")
-        
-        return image
-    
     async def _analyze_with_gemini(self, image, measurement_info, description, timeout, attempt=0):
         """Analyze with Gemini API"""
         prompt = self._create_detailed_prompt(description, measurement_info)
@@ -516,7 +517,7 @@ Standard takhøyde: {measurement_info['ceiling_height']}m
 
 app = FastAPI(
     title="Floor Plan API",
-    version="1.0.6",
+    version="1.0.7",
     docs_url="/"
 )
 
@@ -673,8 +674,9 @@ async def startup_event():
     os.makedirs("logs", exist_ok=True)
     
     logger.info("\n" + "="*60)
-    logger.info("Floor Plan API - Single File Mode")
+    logger.info("Floor Plan API - Optimized Version")
     logger.info(f"Model: gemini-2.5-pro")
+    logger.info(f"With Image Preprocessing: YES")
     logger.info(f"API Key: {'SET' if GOOGLE_API_KEY else 'NOT SET'}")
     logger.info(f"Port: 7860")
     logger.info("="*60 + "\n")