bkgd

app.py

@@ -8,8 +8,6 @@ from typing import List, Optional
 from huggingface_hub import HfApi, hf_hub_download, list_repo_files
 from pathlib import Path
 import tempfile
-import base64
-from rembg import remove
 from auth import verify_hf_token
 
 # HuggingFace configuration
@@ -489,7 +487,14 @@ CRITICAL RULES:
                 'legendary': 'highly detailed and majestic design with dramatic lighting and aura effects'
             }
 
-            full_prompt = f"{image_prompt}\nNow generate an Pokémon Anime image of the monster in an idle pose with a plain dark-grey background. This is a {tier} tier monster with a {tier_descriptions.get(tier, tier_descriptions['medium'])}. The monster should not be attacking or in motion. The full monster must be visible within the frame."
+            background_styles = {
+                'low': 'simple natural environment',
+                'medium': 'detailed thematic natural environment',
+                'high': 'atmospheric thematic environment with special lighting effects',
+                'legendary': 'epic thematic environment with dramatic atmospheric lighting and mystical aura'
+            }
+
+            full_prompt = f"{image_prompt}\nNow generate a Pokémon Anime image of the monster in an idle pose with a {background_styles.get(tier, background_styles['medium'])} background that complements the creature's type and characteristics. This is a {tier} tier monster with a {tier_descriptions.get(tier, tier_descriptions['medium'])}. The monster should not be attacking or in motion. The full monster must be visible and centered in the frame."
 
             print(f"Generating image with prompt: {full_prompt[:100]}...")
             result = client.predict(
@@ -528,44 +533,12 @@ CRITICAL RULES:
             raise Exception(f"Image generation failed: {str(e)}")
 
     @staticmethod
-    def remove_background(image_path: str) -> str:
-        """
-        Remove background from image using rembg
-        Returns base64 encoded PNG with transparency
-
-        Args:
-            image_path: Path to the input image file
-
-        Returns:
-            Base64 encoded string of the image with transparent background
-        """
-        try:
-            print(f"Removing background from image...")
-
-            # Open the image
-            with open(image_path, 'rb') as f:
-                input_image = f.read()
-
-            # Remove background using rembg
-            output_image = remove(input_image)
-
-            # Convert to base64
-            base64_image = base64.b64encode(output_image).decode('utf-8')
-
-            print(f"Background removal completed")
-            return f"data:image/png;base64,{base64_image}"
-
-        except Exception as e:
-            print(f"Failed to remove background: {e}")
-            raise Exception(f"Background removal failed: {str(e)}")
-
-    @staticmethod
-    def upload_image_to_dataset(image_data: str, file_name: str) -> str:
+    def upload_image_to_dataset(image_path: str, file_name: str) -> str:
         """
         Upload image to HuggingFace dataset
 
         Args:
-            image_data: Base64 encoded image data (with or without data URI prefix)
+            image_path: Local path to the image file (or URL to download from)
             file_name: Name for the file (e.g., "pillow_canonical.png")
 
         Returns:
@@ -574,17 +547,17 @@ CRITICAL RULES:
         try:
             print(f"Uploading image to dataset: {file_name}")
 
-            # Remove data URI prefix if present
-            if image_data.startswith('data:'):
-                image_data = image_data.split(',', 1)[1]
-
-            # Decode base64 to bytes
-            image_bytes = base64.b64decode(image_data)
-
-            # Create temporary file
-            with tempfile.NamedTemporaryFile(mode='wb', suffix='.png', delete=False) as f:
-                f.write(image_bytes)
-                temp_path = f.name
+            # Handle both local paths and URLs
+            if image_path.startswith('http'):
+                # Download from URL first
+                import requests
+                response = requests.get(image_path)
+                with tempfile.NamedTemporaryFile(mode='wb', suffix='.png', delete=False) as f:
+                    f.write(response.content)
+                    temp_path = f.name
+            else:
+                # Use local path directly
+                temp_path = image_path
 
             # Upload to HuggingFace dataset
             file_path = f"images/{file_name}"
@@ -596,8 +569,9 @@ CRITICAL RULES:
                 commit_message=f"Add piclet image: {file_name}"
             )
 
-            # Clean up temp file
-            os.unlink(temp_path)
+            # Clean up temp file if we downloaded it
+            if image_path.startswith('http'):
+                os.unlink(temp_path)
 
             # Return the dataset URL
             dataset_url = f"https://huggingface.co/datasets/{DATASET_REPO}/resolve/main/{file_path}"
@@ -924,25 +898,6 @@ def generate_piclet(image, hf_token: str) -> dict:
             hf_token
         )
 
-        # Step 3.5: Remove background from generated image
-        print("Step 3.5/5: Processing image (removing background)...")
-        image_local_path = image_result['imageUrl']
-
-        # Handle both local paths and URLs
-        if image_local_path.startswith('http'):
-            # If it's a URL, download it first
-            import requests
-            response = requests.get(image_local_path)
-            with tempfile.NamedTemporaryFile(mode='wb', suffix='.png', delete=False) as f:
-                f.write(response.content)
-                image_local_path = f.name
-
-        # Remove background
-        transparent_image_base64 = PicletGeneratorService.remove_background(image_local_path)
-
-        # Store the base64 data in image_result for later use
-        image_result['imageData'] = transparent_image_base64
-
         # Step 4: Check for canonical/variation
         print("Step 4/5: Checking for existing canonical...")
         existing_data = PicletDiscoveryService.load_piclet_data(object_name)
@@ -994,7 +949,7 @@ def generate_piclet(image, hf_token: str) -> dict:
             normalized_name = PicletDiscoveryService.normalize_object_name(object_name)
             image_filename = f"{normalized_name}_canonical.png"
             dataset_image_url = PicletGeneratorService.upload_image_to_dataset(
-                image_result['imageData'],
+                image_result['imageUrl'],
                 image_filename
             )
 
@@ -1014,7 +969,6 @@ def generate_piclet(image, hf_token: str) -> dict:
                         "nickname": stats['name'],
                         "stats": stats,
                         "imageUrl": dataset_image_url,
-                        "imageData": image_result['imageData'],
                         "imageCaption": caption,
                         "concept": concept_text,
                         "imagePrompt": image_prompt,
@@ -1044,7 +998,7 @@ def generate_piclet(image, hf_token: str) -> dict:
             # Upload image to dataset with variation filename
             image_filename = f"{normalized_name}_{variation_num:03d}.png"
             dataset_image_url = PicletGeneratorService.upload_image_to_dataset(
-                image_result['imageData'],
+                image_result['imageUrl'],
                 image_filename
             )
 
@@ -1063,7 +1017,6 @@ def generate_piclet(image, hf_token: str) -> dict:
                     "nickname": stats['name'],
                     "stats": stats,
                     "imageUrl": dataset_image_url,
-                    "imageData": image_result['imageData'],
                     "imageCaption": caption,
                     "concept": concept_text,
                     "imagePrompt": image_prompt,
@@ -1097,7 +1050,6 @@ def generate_piclet(image, hf_token: str) -> dict:
                     "nickname": stats['name'],
                     "stats": stats,
                     "imageUrl": image_result.get('imageUrl', ''),
-                    "imageData": image_result.get('imageData', ''),
                     "imageCaption": caption,
                     "concept": concept_text,
                     "imagePrompt": image_prompt,
@@ -1125,7 +1077,6 @@ def generate_piclet(image, hf_token: str) -> dict:
                 "nickname": stats['name'],
                 "stats": stats,
                 "imageUrl": dataset_image_url,
-                "imageData": image_result.get('imageData', ''),
                 "imageCaption": caption,
                 "concept": concept_text,
                 "imagePrompt": image_prompt,

requirements.txt

@@ -3,5 +3,4 @@ gradio_client==1.13.3
 Pillow>=9.0.0
 huggingface_hub>=0.20.0
 datasets>=2.15.0
-requests>=2.31.0
-rembg>=2.0.0
+requests>=2.31.0