Add all project files and updates

.gitignore

@@ -0,0 +1,29 @@
+# Production-ready Python .gitignore
+__pycache__/
+*.py[cod]
+*.pyo
+*.pyd
+*.so
+*.egg
+*.egg-info/
+dist/
+build/
+.eggs/
+.venv*/
+.env
+.env.*
+.envrc
+.DS_Store
+*.log
+*.sqlite3
+*.db
+.cache/
+.gradio/
+*.pem
+*.crt
+*.key
+.idea/
+.vscode/
+*.bak
+*.swp
+*.tmp

Dockerfile

@@ -0,0 +1,13 @@
+# escape=`
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+ENV PYTHONUNBUFFERED=1
+
+CMD ["python", "app.py"]

README.md

@@ -1,13 +1,36 @@
----
-title: Advanced Image Processing Suite
-emoji: 🔥
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.34.1
-app_file: app.py
-pinned: false
-short_description: This is an advanced image processing GenAI application
----
+# Image Processing Suite
+
+This is a production-ready, modular, and extensible image processing application with AI-powered features.
+
+## Features
+- Image format conversion
+- AI image generation (OpenAI, Anthropic, DeepSeek)
+- Image enhancement (filters, super-resolution, etc.)
+- Background removal (local and API)
+- Batch processing
+- Advanced tools (analysis, custom filters, resize/crop)
+
+## Project Structure
+- `src/` — Core application modules (UI, processing, utils)
+- `config/` — Configuration files (env, logging)
+- `tests/` — Unit and integration tests
+- `scripts/` — Deployment, Docker, and utility scripts
+- `requirements.txt` — Python dependencies
+- `app.py` — Entrypoint (will be refactored)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Setup
+1. Create a `.env` file in `config/` (see `.env.example`).
+2. Install dependencies: `pip install -r requirements.txt`
+3. Run: `python app.py`
+
+## Deployment
+- Docker and cloud deployment scripts included in `scripts/`.
+
+## Security
+- API keys and secrets are managed via environment variables.
+
+## Testing
+- Run tests with `pytest` from the root directory.
+
+---
+Built with ❤️ using Gradio, Pillow, OpenCV, rembg, torch, and more.

config/logging_config.py

@@ -0,0 +1,14 @@
+import logging
+import os
+
+def setup_logging():
+    log_level = os.getenv('LOG_LEVEL', 'INFO').upper()
+    logging.basicConfig(
+        level=log_level,
+        format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
+        handlers=[logging.StreamHandler()]
+    )
+    logging.getLogger('PIL').setLevel(logging.WARNING)
+    logging.getLogger('gradio').setLevel(logging.WARNING)
+
+setup_logging()

config/settings.py

@@ -0,0 +1,13 @@
+import os
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv(os.path.join(os.path.dirname(__file__), '.env'))
+
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+ANTHROPIC_API_KEY = os.getenv('ANTHROPIC_API_KEY')
+REMOVE_BG_API_KEY = os.getenv('REMOVE_BG_API_KEY')
+DEEPSEEK_API_KEY = os.getenv('DEEPSEEK_API_KEY')
+GRADIO_SERVER_NAME = os.getenv('GRADIO_SERVER_NAME', '0.0.0.0')
+GRADIO_SERVER_PORT = int(os.getenv('GRADIO_SERVER_PORT', 7860))
+LOG_LEVEL = os.getenv('LOG_LEVEL', 'INFO')

pyproject.toml

@@ -0,0 +1,13 @@
+[tool.ruff]
+line-length = 120
+select = ["E", "F", "W", "I"]
+ignore = ["E501"]
+
+[tool.mypy]
+python_version = "3.11"
+ignore_missing_imports = true
+
+[tool.pytest.ini_options]
+minversion = "6.0"
+addopts = "-ra -q"
+testpaths = ["tests"]

scripts/run_docker.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+# Build and run the app in Docker
+
+docker build -t image-processing-suite .
+docker run -d -p 7860:7860 --env-file ./config/.env image-processing-suite

src/ai_image_generator.py

@@ -0,0 +1,77 @@
+import requests
+import logging
+from PIL import Image
+import io
+from pathlib import Path
+
+class AIImageGenerator:
+    def __init__(self, openai_key=None, anthropic_key=None):
+        self.openai_key = openai_key
+        self.anthropic_key = anthropic_key
+
+    def generate_image_openai(self, prompt, model="dall-e-3", size="1024x1024"):
+        if not self.openai_key:
+            return None, "❌ OpenAI API key not provided"
+        try:
+            headers = {
+                "Authorization": f"Bearer {self.openai_key}",
+                "Content-Type": "application/json"
+            }
+            data = {
+                "model": model,
+                "prompt": prompt,
+                "size": size,
+                "n": 1
+            }
+            response = requests.post(
+                "https://api.openai.com/v1/images/generations",
+                headers=headers,
+                json=data,
+                timeout=60
+            )
+            if response.status_code == 200:
+                result = response.json()
+                image_url = result["data"][0]["url"]
+                img_response = requests.get(image_url)
+                img = Image.open(io.BytesIO(img_response.content))
+                output_path = f"generated_image_{hash(prompt) % 10000}.png"
+                img.save(output_path)
+                return output_path, f"✅ Image generated successfully with {model}"
+            else:
+                return None, f"❌ OpenAI API Error: {response.text}"
+        except Exception as e:
+            logging.exception("OpenAI image generation failed")
+            return None, f"❌ Error generating image: {str(e)}"
+
+    def generate_image_anthropic(self, prompt):
+        if not self.anthropic_key:
+            return None, "❌ Anthropic API key not provided"
+        try:
+            headers = {
+                "x-api-key": self.anthropic_key,
+                "Content-Type": "application/json",
+                "anthropic-version": "2023-06-01"
+            }
+            data = {
+                "model": "claude-3-5-sonnet-20241022",
+                "max_tokens": 1024,
+                "messages": [{
+                    "role": "user",
+                    "content": f"Create a detailed visual description for an AI image generator based on this prompt: {prompt}. Make it artistic and detailed."
+                }]
+            }
+            response = requests.post(
+                "https://api.anthropic.com/v1/messages",
+                headers=headers,
+                json=data,
+                timeout=30
+            )
+            if response.status_code == 200:
+                result = response.json()
+                enhanced_prompt = result["content"][0]["text"]
+                return None, f"✅ Enhanced prompt created: {enhanced_prompt[:200]}..."
+            else:
+                return None, f"❌ Anthropic API Error: {response.text}"
+        except Exception as e:
+            logging.exception("Anthropic image generation failed")
+            return None, f"❌ Error with Anthropic API: {str(e)}"

src/background_removal.py

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+import requests
+import logging
+from PIL import Image
+import io
+from pathlib import Path
+
+class BackgroundRemover:
+    def __init__(self, removebg_key=None):
+        self.removebg_key = removebg_key
+
+    def remove_local(self, input_path):
+        from rembg import remove
+        try:
+            img = Image.open(input_path)
+            result = remove(img)
+            out_path = Path(input_path).with_name(f"nobg_{Path(input_path).name}")
+            result.save(out_path)
+            return str(out_path), "✅ Background removed locally"
+        except Exception as e:
+            logging.exception("Local background removal failed")
+            return None, f"❌ Background removal error: {str(e)}"
+
+    def remove_with_removebg(self, input_path):
+        if not self.removebg_key:
+            return None, "❌ Remove.bg API key not provided"
+        try:
+            with open(input_path, 'rb') as img_file:
+                response = requests.post(
+                    'https://api.remove.bg/v1.0/removebg',
+                    files={'image_file': img_file},
+                    data={'size': 'auto'},
+                    headers={'X-Api-Key': self.removebg_key},
+                    timeout=30
+                )
+            if response.status_code == 200:
+                out_path = Path(input_path).with_name(f"removebg_{Path(input_path).name}")
+                with open(out_path, 'wb') as out_file:
+                    out_file.write(response.content)
+                return str(out_path), "✅ Background removed with Remove.bg"
+            else:
+                return None, f"❌ Remove.bg API Error: {response.text}"
+        except Exception as e:
+            logging.exception("Remove.bg API background removal failed")
+            return None, f"❌ Remove.bg error: {str(e)}"

src/custom_filters.py

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+from PIL import Image, ImageEnhance, ImageOps
+import numpy as np
+from pathlib import Path
+import colorsys
+import logging
+
+def apply_custom_filter(img_path, bright, cont, sat, hue):
+    if not img_path:
+        return None, "❌ Please upload an image"
+    try:
+        img = Image.open(img_path)
+        if bright != 0:
+            enhancer = ImageEnhance.Brightness(img)
+            img = enhancer.enhance(1 + bright / 100)
+        if cont != 0:
+            enhancer = ImageEnhance.Contrast(img)
+            img = enhancer.enhance(1 + cont / 100)
+        if sat != 0:
+            enhancer = ImageEnhance.Color(img)
+            img = enhancer.enhance(1 + sat / 100)
+        if hue != 0 and img.mode == "RGB":
+            img_array = np.array(img)
+            hsv = np.array([colorsys.rgb_to_hsv(r/255, g/255, b/255) for r, g, b in img_array.reshape(-1, 3)])
+            hsv[:, 0] = (hsv[:, 0] + hue / 360) % 1.0
+            rgb = np.array([colorsys.hsv_to_rgb(h, s, v) for h, s, v in hsv])
+            img_array = (rgb * 255).astype(np.uint8).reshape(img_array.shape)
+            img = Image.fromarray(img_array)
+        out_path = Path(img_path).with_name(f"filtered_{Path(img_path).name}")
+        img.save(out_path)
+        return str(out_path), "✅ Custom filter applied successfully"
+    except Exception as e:
+        logging.exception("Custom filter failed")
+        return None, f"❌ Filter error: {str(e)}"

src/image_analysis.py

@@ -0,0 +1,32 @@
+import logging
+from PIL import Image
+import numpy as np
+from pathlib import Path
+
+class ImageAnalysis:
+    @staticmethod
+    def analyze_image(img_path):
+        if not img_path:
+            return {"error": "No image provided"}
+        try:
+            img = Image.open(img_path)
+            analysis = {
+                "dimensions": f"{img.width} x {img.height}",
+                "format": img.format,
+                "mode": img.mode,
+                "file_size": f"{Path(img_path).stat().st_size / 1024:.1f} KB",
+                "has_transparency": img.mode in ("RGBA", "LA") or "transparency" in img.info,
+                "color_palette": "Analyzed" if img.mode == "P" else "N/A"
+            }
+            if img.mode == "RGB":
+                img_array = np.array(img)
+                analysis["average_color"] = {
+                    "red": int(np.mean(img_array[:,:,0])),
+                    "green": int(np.mean(img_array[:,:,1])),
+                    "blue": int(np.mean(img_array[:,:,2]))
+                }
+                analysis["brightness"] = int(np.mean(img_array))
+            return analysis
+        except Exception as e:
+            logging.exception("Image analysis failed")
+            return {"error": f"Analysis failed: {str(e)}"}

src/image_processing.py

@@ -0,0 +1,106 @@
+from PIL import Image, ImageEnhance, ImageFilter, ImageOps
+import numpy as np
+import cv2
+from pathlib import Path
+from rembg import remove
+import logging
+
+class ImageProcessor:
+    def convert_image(self, input_path, output_format, quality=95):
+        try:
+            img = Image.open(input_path)
+            if output_format.upper() in ["JPEG", "JPG", "BMP", "PDF"]:
+                if img.mode in ("RGBA", "LA", "P"):
+                    background = Image.new("RGB", img.size, (255, 255, 255))
+                    if img.mode == "P":
+                        img = img.convert("RGBA")
+                    background.paste(img, mask=img.split()[-1] if img.mode in ("RGBA", "LA") else None)
+                    img = background
+                else:
+                    img = img.convert("RGB")
+            elif output_format.upper() == "PNG":
+                if img.mode != "RGBA":
+                    img = img.convert("RGBA")
+            elif output_format.upper() in ["TIFF", "TIF"]:
+                pass
+            else:
+                img = img.convert("RGB")
+            out_path = Path(input_path).with_suffix(f".{output_format.lower()}")
+            save_kwargs = {}
+            if output_format.upper() in ["JPEG", "JPG"]:
+                save_kwargs = {"quality": quality, "optimize": True}
+            elif output_format.upper() == "PNG":
+                save_kwargs = {"optimize": True}
+            elif output_format.upper() == "WEBP":
+                save_kwargs = {"quality": quality, "method": 6}
+            img.save(out_path, format=output_format.upper(), **save_kwargs)
+            return str(out_path), f"✅ Converted to {output_format.upper()}"
+        except Exception as e:
+            logging.exception("Image conversion failed")
+            return None, f"❌ Error: {str(e)}"
+
+    def enhance_image(self, input_path, enhancement_type, intensity=1.0):
+        try:
+            img = Image.open(input_path)
+            if enhancement_type == "AI Super Resolution":
+                img = img.resize((img.width * 2, img.height * 2), Image.LANCZOS)
+            elif enhancement_type == "Noise Reduction":
+                cv_img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+                denoised = cv2.fastNlMeansDenoisingColored(cv_img, None, 10, 10, 7, 21)
+                img = Image.fromarray(cv2.cvtColor(denoised, cv2.COLOR_BGR2RGB))
+            elif enhancement_type == "Color Enhancement":
+                enhancer = ImageEnhance.Color(img)
+                img = enhancer.enhance(1.0 + intensity * 0.5)
+            elif enhancement_type == "Brightness/Contrast":
+                brightness = ImageEnhance.Brightness(img)
+                img = brightness.enhance(1.0 + intensity * 0.2)
+                contrast = ImageEnhance.Contrast(img)
+                img = contrast.enhance(1.0 + intensity * 0.3)
+            elif enhancement_type == "Sharpening":
+                enhancer = ImageEnhance.Sharpness(img)
+                img = enhancer.enhance(1.0 + intensity)
+            elif enhancement_type == "HDR Effect":
+                img_array = np.array(img, dtype=np.float32) / 255.0
+                img_array = np.power(img_array, 0.5 + intensity * 0.3)
+                img = Image.fromarray((img_array * 255).astype(np.uint8))
+            elif enhancement_type == "Black & White":
+                img = img.convert("L").convert("RGB")
+            elif enhancement_type == "Sepia":
+                img = ImageOps.colorize(img.convert("L"), "#704214", "#C8B99C")
+            elif enhancement_type == "Vintage Filter":
+                img = ImageEnhance.Contrast(img).enhance(0.8)
+                img = ImageEnhance.Brightness(img).enhance(1.1)
+                img = ImageEnhance.Color(img).enhance(0.7)
+            elif enhancement_type == "Vignette":
+                mask = Image.new("L", img.size, 0)
+                center_x, center_y = img.size[0] // 2, img.size[1] // 2
+                max_dist = min(center_x, center_y)
+                for y in range(img.size[1]):
+                    for x in range(img.size[0]):
+                        dist = ((x - center_x) ** 2 + (y - center_y) ** 2) ** 0.5
+                        alpha = max(0, 255 - int(255 * dist / max_dist * intensity))
+                        mask.putpixel((x, y), alpha)
+                img.putalpha(mask)
+                background = Image.new("RGB", img.size, (0, 0, 0))
+                background.paste(img, img)
+                img = background
+            out_path = Path(input_path).with_name(f"enhanced_{Path(input_path).name}")
+            img.save(out_path)
+            return str(out_path), f"✅ Applied {enhancement_type} enhancement"
+        except Exception as e:
+            logging.exception("Image enhancement failed")
+            return None, f"❌ Enhancement error: {str(e)}"
+
+    def remove_background(self, input_path, service="local"):
+        try:
+            if service == "local":
+                img = Image.open(input_path)
+                result = remove(img)
+                out_path = Path(input_path).with_name(f"nobg_{Path(input_path).name}")
+                result.save(out_path)
+                return str(out_path), "✅ Background removed locally"
+            else:
+                return None, f"❌ Service {service} not implemented in this module"
+        except Exception as e:
+            logging.exception("Background removal failed")
+            return None, f"❌ Background removal error: {str(e)}"

src/resize_crop.py

@@ -0,0 +1,50 @@
+from PIL import Image
+from pathlib import Path
+import logging
+
+def resize_crop_image(img_path, mode, width, height, maintain_ratio, quality):
+    if not img_path:
+        return None, "❌ Please upload an image"
+    try:
+        img = Image.open(img_path)
+        original_size = img.size
+        quality_filter = getattr(Image, quality, Image.LANCZOS)
+        if mode == "Resize":
+            if maintain_ratio:
+                img.thumbnail((int(width), int(height)), quality_filter)
+            else:
+                img = img.resize((int(width), int(height)), quality_filter)
+        elif mode == "Crop":
+            crop_width, crop_height = int(width), int(height)
+            left = (img.width - crop_width) // 2
+            top = (img.height - crop_height) // 2
+            right = left + crop_width
+            bottom = top + crop_height
+            img = img.crop((left, top, right, bottom))
+        elif mode == "Smart Crop":
+            target_ratio = width / height
+            current_ratio = img.width / img.height
+            if current_ratio > target_ratio:
+                new_width = int(img.height * target_ratio)
+                left = (img.width - new_width) // 2
+                img = img.crop((left, 0, left + new_width, img.height))
+            else:
+                new_height = int(img.width / target_ratio)
+                top = (img.height - new_height) // 2
+                img = img.crop((0, top, img.width, top + new_height))
+            img = img.resize((int(width), int(height)), quality_filter)
+        elif mode == "Canvas Resize":
+            canvas = Image.new("RGB", (int(width), int(height)), (255, 255, 255))
+            paste_x = (int(width) - img.width) // 2
+            paste_y = (int(height) - img.height) // 2
+            if img.mode == "RGBA":
+                canvas.paste(img, (paste_x, paste_y), img)
+            else:
+                canvas.paste(img, (paste_x, paste_y))
+            img = canvas
+        out_path = Path(img_path).with_name(f"{mode.lower()}_{Path(img_path).name}")
+        img.save(out_path)
+        return str(out_path), f"✅ {mode} completed: {original_size} → {img.size}"
+    except Exception as e:
+        logging.exception("Resize/crop failed")
+        return None, f"❌ Processing error: {str(e)}"

tests/test_placeholder.py

@@ -0,0 +1,8 @@
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import pytest
+
+def test_placeholder():
+    assert True