app.py

import os
import gradio as gr
import google.generativeai as genai
from PIL import Image, ImageEnhance, ImageFilter
import io
import base64
import json
import time
import traceback
try:
    import pyperclip
except ImportError:
    pyperclip = None

# --- Environment Configuration ---
GEMINI_KEY = os.environ.get("GEMINI_KEY", "")
DEFAULT_PORT = int(os.environ.get("PORT", 7860))

# --- Style Template Optimization ---
BASE_TEMPLATE = """Describe this design as a concise Flux 1.1 Pro prompt focusing on:
- Key visual elements
- Technical specifications
- Style consistency
- Functional requirements"""

STYLE_INSTRUCTIONS = {
    "General": BASE_TEMPLATE,
    "Realistic": f"{BASE_TEMPLATE}\nPHOTOREALISTIC RULES: Use photography terms, texture details, accurate lighting",
    "Kawaii": f"{BASE_TEMPLATE}\nKAWAII RULES: Rounded shapes, pastel colors, cute expressions",
    "Vector": f"{BASE_TEMPLATE}\nVECTOR RULES: Clean lines, geometric shapes, B&W gradients",
    "Silhouette": f"{BASE_TEMPLATE}\nSILHOUETTE RULES: High contrast, minimal details, strong outlines"
}

# --- Flux Configuration ---
FLUX_SPECS = {
    "aspect_ratios": ["1:1", "16:9", "4:3", "9:16"],
    "formats": ["SVG", "PNG", "PDF"],
    "color_modes": ["B&W", "CMYK", "RGB"],
    "dpi_options": [72, 150, 300, 600]
}

# --- Quality Control System ---
class QualityValidator:
    VALIDATION_TEMPLATE = """Analyze this Flux prompt:
1. Score style adherence (1-5)
2. List technical issues
3. Suggest improvements
Respond ONLY as JSON: {"score": x/10, "issues": [], "suggestions": []}"""

    @classmethod
    def validate(cls, prompt, model):
        try:
            response = model.generate_content([cls.VALIDATION_TEMPLATE, prompt])
            return json.loads(response.text)
        except:
            return {"score": 0, "issues": ["Validation failed"], "suggestions": []}

# --- Image Processing Pipeline ---
def preprocess_image(img):
    """Convert and enhance uploaded images"""
    try:
        if isinstance(img, str):  # Handle file paths
            img = Image.open(img)
        img = img.convert("RGB")
        img = ImageEnhance.Contrast(img).enhance(1.2)
        img = img.filter(ImageFilter.SHARPEN)
        return img
    except Exception as e:
        raise ValueError(f"Image processing error: {str(e)}")

# --- Core Generation Engine ---
def generate_prompt(image, api_key, style, creativity, neg_prompt, aspect, color_mode, dpi):
    try:
        # Validate inputs
        if not image:
            raise ValueError("Please upload an image")
            
        api_key = api_key or GEMINI_KEY
        if not api_key:
            raise ValueError("API key required - set in env (GEMINI_KEY) or input field")

        # Initialize model
        genai.configure(api_key=api_key)
        model = genai.GenerativeModel("gemini-1.5-pro")

        # Process image
        img = preprocess_image(image)
        img_bytes = io.BytesIO()
        img.save(img_bytes, format="PNG")
        img_b64 = base64.b64encode(img_bytes.getvalue()).decode()

        # Build instruction
        instruction = f"{STYLE_INSTRUCTIONS[style]}\nAVOID: {neg_prompt}\n"
        instruction += f"ASPECT: {aspect}, COLORS: {color_mode}, DPI: {dpi}\n"

        # Generate prompt
        response = model.generate_content(
            contents=[instruction, {"mime_type": "image/png", "data": img_b64}],
            generation_config={"temperature": creativity}
        )
        raw_prompt = response.text

        # Quality validation
        validation = QualityValidator.validate(raw_prompt, model)
        if validation.get("score", 0) < 7:
            response = model.generate_content(f"Improve this prompt: {raw_prompt}\nIssues: {validation['issues']}")
            raw_prompt = response.text

        # Token tracking
        input_tokens = len(img_b64) // 4  # Approximate base64 token count
        output_tokens = len(raw_prompt.split())
        
        return {
            "prompt": raw_prompt,
            "validation": validation,
            "stats": {"input": input_tokens, "output": output_tokens}
        }

    except Exception as e:
        traceback.print_exc()
        return {"error": str(e)}

# --- UI Components ---
def create_advanced_controls():
    with gr.Accordion("⚙️ Advanced Settings", open=False):
        with gr.Row():
            creativity = gr.Slider(0.0, 1.0, 0.7, label="Creativity Level")
            neg_prompt = gr.Textbox(label="🚫 Negative Prompts", placeholder="What to avoid")
        with gr.Row():
            aspect = gr.Dropdown(FLUX_SPECS["aspect_ratios"], label="Aspect Ratio")
            color_mode = gr.Dropdown(FLUX_SPECS["color_modes"], label="Color Mode")
            dpi = gr.Dropdown(FLUX_SPECS["dpi_options"], label="Output DPI")
    return [creativity, neg_prompt, aspect, color_mode, dpi]

# --- Main Interface ---
def build_interface():
    with gr.Blocks(title="Flux Pro Generator", theme=gr.themes.Soft()) as app:
        # Security Section
        api_key = gr.Textbox(
            label="🔑 Gemini API Key",
            value=GEMINI_KEY,
            type="password",
            info="Set GEMINI_KEY environment variable for production"
        )

        # Main Workflow
        with gr.Row(variant="panel"):
            with gr.Column(scale=1):
                img_input = gr.Image(
                    label="🖼️ Upload Design",
                    type="pil",
                    sources=["upload"],
                    interactive=True
                )
                style = gr.Dropdown(
                    list(STYLE_INSTRUCTIONS.keys()),
                    value="General",
                    label="🎨 Target Style"
                )
                adv_controls = create_advanced_controls()
                gen_btn = gr.Button("✨ Generate Prompt", variant="primary")

            with gr.Column(scale=2):
                prompt_output = gr.Textbox(
                    label="📝 Optimized Prompt",
                    lines=8,
                    interactive=False
                )
                with gr.Row():
                    copy_btn = gr.Button("📋 Copy")
                    history_btn = gr.Button("🕒 History")
                quality_report = gr.JSON(
                    label="🔍 Quality Report",
                    visible=True
                )
                token_stats = gr.JSON(
                    label="🧮 Token Usage",
                    visible=True
                )

        # History System
        history = gr.State([])

        # Event Handling
        gen_btn.click(
            lambda *args: generate_prompt(*args),
            inputs=[img_input, api_key, style] + adv_controls,
            outputs=[prompt_output, quality_report, token_stats]
        )

        copy_btn.click(
            lambda x: pyperclip.copy(x) if pyperclip else None,
            inputs=prompt_output,
            outputs=None
        )

    return app

# --- Production Launch ---
if __name__ == "__main__":
    app = build_interface()
    app.launch()