Update app.py

app.py

@@ -1,103 +1,171 @@
+import os
 import gradio as gr
 import google.generativeai as genai
-from PIL import Image
+from PIL import Image, ImageEnhance, ImageFilter
 import io
 import base64
+import json
+import time
+try:
+    import pyperclip
+except ImportError:
+    os.system('pip install pyperclip')
+    import pyperclip
+
+# --- Security First Configuration ---
+GEMINI_KEY = os.environ.get("GEMINI_KEY", "")  # Load from environment variable
+
+# --- Template Optimization System ---
+BASE_TEMPLATE = (
+    "Describe this design as a single, cohesive, and concise prompt suitable for Flux 1.1 Pro. "
+    "Focus on key elements such as text, symbols, layout, and overall style."
+)
 
-# Mapping styles to instructions
 STYLE_INSTRUCTIONS = {
-    "General": (
-        "Describe this design as a single, cohesive, and concise prompt suitable for Flux 1.1 Pro. "
-        "Focus on key elements such as text, symbols, layout, and overall style."
-    ),
+    "General": BASE_TEMPLATE,
     "Realistic": (
-        "Describe this design with a focus on photorealistic details, including textures, lighting, and depth, "
-        "ensuring accuracy in representation. Avoid illustrations or cartoon-like interpretations."
+        f"{BASE_TEMPLATE} Focus on photorealistic details: textures, lighting, depth. "
+        "Avoid illustrations/cartoon styles. Use technical photography terms."
     ),
     "Kawaii/Cartoon": (
-        "Describe this design with an emphasis on cute, rounded shapes, playful expressions, and vibrant, fun aesthetics "
-        "typical of the kawaii/cartoon style. Focus on light-hearted and visually charming elements."
+        f"{BASE_TEMPLATE} Emphasize cute, rounded shapes, playful expressions, "
+        "vibrant colors. Use anime/kawaii terminology."
     ),
     "Vector": (
-        "Describe this design as a single, cohesive, and concise prompt suitable for Flux 1.1 Pro. "
-        "Focus on key elements such as text, symbols, layout, and overall style. "
-        "Specify that the design should be created in a clean, professional vector and illustration style, "
-        "emphasizing sharp lines, geometric shapes, and smooth gradients where applicable. "
-        "The entire design must be in black and white, using only shades of gray for depth and contrast. "
-        "Avoid any use of color unless it is essential to the design's purpose. "
-        "Avoid cartoonish or overly stylized elements unless they align with the design's purpose. "
-        "Use clear and direct language to convey the design's theme, humor, or purpose, if applicable. "
-        "Ensure the description is compact, well-structured, and optimized for creating a graphic. "
-        "Include only essential details and avoid unnecessary elaboration."
+        f"{BASE_TEMPLATE} Specify clean vector style with sharp lines, geometric shapes. "
+        "Black/white only with gray gradients. Use design technical terms."
     ),
     "Silhouette": (
-        "Describe this design using high-contrast black and white silhouettes, focusing on bold shapes and clear outlines "
-        "to convey the subject effectively. Eliminate interior details unless crucial to recognition."
+        f"{BASE_TEMPLATE} Use high-contrast black/white silhouettes. "
+        "Focus on bold shapes and outlines. Eliminate interior details."
     ),
 }
 
-# Function to check API key validity
-def check_api(api_key):
-    try:
-        genai.configure(api_key=api_key)
-        model = genai.GenerativeModel("gemini-1.5-pro")
-        response = model.generate_content("Hello, can you respond?")
-        return "✅ API Key is valid!" if response.text else "⚠️ API responded but empty."
-    except Exception as e:
-        return f"❌ Error: {str(e)}"
-
-# Function to generate a prompt using Gemini
-def generate_prompt(image, api_key, style):
-    if not api_key:
-        return "❌ Error: Please enter your Google Gemini API key."
-
-    try:
-        genai.configure(api_key=api_key)
-        model = genai.GenerativeModel("gemini-1.5-pro")
-
-        # Convert image to base64
-        image_bytes = io.BytesIO()
-        image.save(image_bytes, format=image.format if image.format else "PNG")
-        image_base64 = base64.b64encode(image_bytes.getvalue()).decode('utf-8')
-
-        instruction = STYLE_INSTRUCTIONS.get(style, STYLE_INSTRUCTIONS["General"])
+# --- Flux Configuration Engine ---
+FLUX_SPECS = {
+    "aspect_ratios": ["1:1", "16:9", "4:3", "9:16", "Custom"],
+    "output_formats": ["SVG", "PNG-300dpi", "PDF", "EPS"],
+    "color_modes": ["B&W", "CMYK", "Spot Colors", "RGB"],
+    "complexity_levels": ["Minimal", "Medium", "High", "Ultra"]
+}
 
+# --- Quality Control Systems ---
+class QualityValidator:
+    VALIDATION_PROMPT = """Analyze this Flux prompt on:
+1. Technical specificity (1-5)
+2. Style adherence (1-5)
+3. Flux compatibility (1-5)
+Respond ONLY as JSON: {"total": x/15, "issues": [list]}"""
+
+    @classmethod
+    def validate(cls, prompt, model):
+        try:
+            response = model.generate_content([cls.VALIDATION_PROMPT, prompt])
+            return json.loads(response.text)
+        except:
+            return {"total": 0, "issues": ["Validation failed"]}
+
+# --- Image Processing Pipeline ---
+def preprocess_image(img):
+    """Enhance image quality before analysis"""
+    img = img.convert("RGB")
+    img = ImageEnhance.Contrast(img).enhance(1.2)
+    img = img.filter(ImageFilter.SHARPEN)
+    return img
+
+# --- Core Application Logic ---
+def generate_prompt(image, api_key, style, creativity, neg_prompt, flux_specs):
+    genai.configure(api_key=api_key or GEMINI_KEY)
+    model = genai.GenerativeModel("gemini-1.5-pro", generation_config={
+        "temperature": creativity,
+        "top_p": 0.95
+    })
+
+    # Pre-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}\nFLUX SPECS: {flux_specs}"
+
+    # Generate initial prompt
+    response = model.generate_content([instruction, {"mime_type": "image/png", "data": img_b64}])
+    raw_prompt = response.text
+
+    # Quality validation
+    validation = QualityValidator.validate(raw_prompt, model)
+    if validation["total"] < 10:  # Regenerate if low quality
         response = model.generate_content([
-            instruction,
-            {"mime_type": "image/png", "data": image_base64}
+            f"Improve this prompt addressing: {validation['issues']}\n\n{raw_prompt}"
         ])
+        raw_prompt = response.text
 
-        return response.text if response.text else "⚠️ No response generated."
-    except Exception as e:
-        return f"❌ Error generating prompt: {str(e)}"
-
-# Gradio Interface
-def main():
-    with gr.Blocks() as app:
-        gr.Markdown("## 🎨 Flux Prompt Generator with Gemini 1.5 Pro")
-
-        api_key = gr.Textbox(label="🔑 Google Gemini API Key", type="password", placeholder="Paste your Gemini API key here")
-        check_button = gr.Button("✅ Check API Key")
-        check_output = gr.Textbox(label="API Key Status")
+    return raw_prompt, validation
 
+# --- UI Components ---
+def create_advanced_params():
+    with gr.Accordion("⚙️ Advanced Parameters", open=False):
         with gr.Row():
-            with gr.Column():
-                image_input = gr.Image(label="📷 Upload Design Image", type="pil", width=300)
-                style_dropdown = gr.Dropdown(label="🎨 Style", choices=list(STYLE_INSTRUCTIONS.keys()), value="General")
-
-        generate_button = gr.Button("🧠 Generate Prompt")
-        prompt_output = gr.Textbox(label="📝 Generated Flux Prompt", lines=6)
-        copy_button = gr.Button("📋 Copy to Clipboard")
-
-        def copy_prompt(prompt):
-            return gr.update(value=prompt, interactive=True)
-
-        check_button.click(check_api, inputs=api_key, outputs=check_output)
-        generate_button.click(generate_prompt, inputs=[image_input, api_key, style_dropdown], outputs=prompt_output)
-        copy_button.click(copy_prompt, inputs=prompt_output, outputs=prompt_output)
+            creativity = gr.Slider(0.0, 1.0, 0.7, label="Creativity")
+            neg_prompt = gr.Textbox(label="🚫 Avoid in Prompt", placeholder="e.g., no text, avoid gradients")
+        with gr.Row():
+            aspect = gr.Dropdown(FLUX_SPECS["aspect_ratios"], value="1:1", label="Aspect Ratio")
+            fmt = gr.Dropdown(FLUX_SPECS["output_formats"], value="SVG", label="Output Format")
+            color = gr.Dropdown(FLUX_SPECS["color_modes"], value="B&W", label="Color Mode")
+            complexity = gr.Dropdown(FLUX_SPECS["complexity_levels"], value="Medium", label="Complexity")
+    return [creativity, neg_prompt, aspect, fmt, color, complexity]
+
+# --- Gradio Interface ---
+def build_interface():
+    with gr.Blocks(title="Flux Pro Generator") as app:
+        # Security First
+        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():
+            with gr.Column(scale=1):
+                img_input = gr.Image(label="🖼️ Upload Design", type="pil", sources=["upload", "clipboard"])
+                style = gr.Dropdown(list(STYLE_INSTRUCTIONS), value="General", label="🎨 Target Style")
+                adv_params = create_advanced_params()
+                
+            with gr.Column(scale=2):
+                prompt_output = gr.Textbox(label="📝 Optimized Prompt", lines=8, interactive=False)
+                with gr.Row():
+                    gen_btn = gr.Button("✨ Generate", variant="primary")
+                    copy_btn = gr.Button("📋 Copy")
+                quality_report = gr.JSON(label="🔍 Quality Report")
+
+        # Enterprise Features
+        token_counter = gr.Textbox(label="🧮 Token Usage", interactive=False)
+        history = gr.State([])
+
+        # Event Handling
+        gen_btn.click(
+            generate_prompt,
+            inputs=[img_input, api_key, style] + adv_params,
+            outputs=[prompt_output, quality_report]
+        )
+        
+        copy_btn.click(
+            lambda x: (pyperclip.copy(x), x),
+            inputs=prompt_output,
+            outputs=prompt_output
+        )
 
     return app
 
-# Launch the app
-app = main()
-app.launch()
+# --- Production Deployment ---
+if __name__ == "__main__":
+    app = build_interface()
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=int(os.getenv("PORT", 7860)),
+        share=False,
+        auth=(
+            os.getenv("APP_USER", "admin"), 
+            os.getenv("APP_PWD", "admin")
+        ) if os.getenv("ENV") == "prod" else None
+    )