app.py

import streamlit as st
import os
import json
import uuid
from datetime import datetime, timezone
from pathlib import Path
from providers.pollinations_provider import PollinationsProvider
from providers.hf_provider import HuggingFaceProvider
from providers.stub_provider import StubProvider
from dotenv import load_dotenv

load_dotenv()

# ==============================================
# PATHS & CONFIGURATION
# ==============================================
BASE_DIR = Path(__file__).parent
STORAGE_DIR = BASE_DIR / "storage"
PROJECTS_DIR = BASE_DIR / "projects"
PROMPT_HISTORY = BASE_DIR / "prompt_history.json"
FEEDBACK_DB = BASE_DIR / "feedback_db.json"

# Create directories
STORAGE_DIR.mkdir(exist_ok=True)
PROJECTS_DIR.mkdir(exist_ok=True)

# Initialize JSON files
if not PROMPT_HISTORY.exists():
    PROMPT_HISTORY.write_text("[]")
if not FEEDBACK_DB.exists():
    FEEDBACK_DB.write_text("[]")

# ==============================================
# PROVIDERS CONFIGURATION
# ==============================================
PROVIDERS = {
    "FLUX (Pollinations - FREE)": ("pollinations", "flux"),
    "Turbo (Pollinations - FREE)": ("pollinations", "turbo"),
    "Stable Diffusion 1.4 (HF)": ("huggingface", "CompVis/stable-diffusion-v1-4"),
    "Stable Diffusion 1.5 (HF)": ("huggingface", "runwayml/stable-diffusion-v1-5"),
    "Demo Mode": ("stub", "demo"),
}

VIDEO_PROVIDERS = {
    "RunwayML Gen-2 — Demo": ("stub", "runway_gen2"),
    "Stable Video Diffusion — Demo": ("stub", "svd"),
    "Pika Labs — Coming Soon": ("stub", "pika"),
}

MODEL_3D_PROVIDERS = {
    "Shap-E (OpenAI) — Demo": ("stub", "shap_e"),
    "Point-E — Demo": ("stub", "point_e"),
    "DreamFusion — Coming Soon": ("stub", "dreamfusion"),
}

def get_provider(kind, model):
    """Factory function to get appropriate provider"""
    if kind == "huggingface":
        return HuggingFaceProvider(model=model)
    elif kind == "pollinations":
        return PollinationsProvider(model=model)
    elif kind == "stub":
        return StubProvider(name=model)
    else:
        return StubProvider(name="Unknown")

# ==============================================
# HELPER FUNCTIONS
# ==============================================
def save_to_history(asset_type, prompt, negative_prompt, provider, model_name, results, metadata=None):
    """Save generation to history"""
    try:
        history_content = PROMPT_HISTORY.read_text()
        history = json.loads(history_content) if history_content else []
    except json.JSONDecodeError:
        history = []

    entry = {
        "id": uuid.uuid4().hex,
        "asset_type": asset_type,
        "prompt": prompt,
        "negative_prompt": negative_prompt,
        "provider": provider,
        "model_name": model_name,
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "results": results,
        "metadata": metadata or {},
        "feedback": []
    }
    history.insert(0, entry)
    PROMPT_HISTORY.write_text(json.dumps(history, indent=2))
    return entry["id"]

def add_feedback(entry_id, feedback_text, rating):
    """Add feedback to a generation"""
    try:
        history = json.loads(PROMPT_HISTORY.read_text())
    except json.JSONDecodeError:
        return

    for entry in history:
        if entry["id"] == entry_id:
            entry["feedback"].append({
                "text": feedback_text,
                "rating": rating,
                "timestamp": datetime.now(timezone.utc).isoformat()
            })
    PROMPT_HISTORY.write_text(json.dumps(history, indent=2))

def create_project(name, brief):
    """Create a new project"""
    project = {
        "id": uuid.uuid4().hex,
        "name": name,
        "brief": brief,
        "created_at": datetime.now(timezone.utc).isoformat(),
        "assets": []
    }
    project_file = PROJECTS_DIR / f"{project['id']}.json"
    project_file.write_text(json.dumps(project, indent=2))
    return project

# ==============================================
# STREAMLIT UI CONFIGURATION
# ==============================================
st.set_page_config(
    page_title="AI Studio — Creative Workflow Platform",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS
st.markdown("""
<style>
    .stTabs [data-baseweb="tab-list"] {
        gap: 24px;
    }
    .stTabs [data-baseweb="tab"] {
        height: 50px;
        padding-left: 20px;
        padding-right: 20px;
    }
    .asset-card {
        border: 1px solid #ddd;
        border-radius: 8px;
        padding: 16px;
        margin: 8px 0;
    }
</style>
""", unsafe_allow_html=True)

# ==============================================
# SIDEBAR
# ==============================================
st.sidebar.title("🎨 AI Studio Platform")
st.sidebar.markdown("### Creative Workflow Builder")

# Project selection
st.sidebar.markdown("---")
st.sidebar.subheader("Current Project")
project_name = st.sidebar.text_input("Project Name", value="Luxury Real Estate Campaign")
project_brief = st.sidebar.text_area("Creative Brief", 
    value="High-end real estate marketing campaign targeting luxury buyers. Focus on premium aesthetics, lifestyle, and architectural excellence.")

if st.sidebar.button("Create New Project"):
    proj = create_project(project_name, project_brief)
    st.sidebar.success(f"Project created: {proj['name']}")
    
# ==============================================
# EXCEL UPLOAD MODULE
# ==============================================
st.sidebar.markdown("---")
st.sidebar.subheader("📊 Excel Batch Import")

uploaded_file = st.sidebar.file_uploader("Upload Excel", type=['xlsx', 'xls'])

if uploaded_file is not None:
    try:
        from utils.prompt_enhancer import load_excel_to_projects
        
        # Save uploaded file temporarily
        temp_path = STORAGE_DIR / "temp_upload.xlsx"
        with open(temp_path, "wb") as f:
            f.write(uploaded_file.getbuffer())
        
        # Load projects
        projects = load_excel_to_projects(temp_path)
        st.session_state['uploaded_projects'] = projects
        
        st.sidebar.success(f"✅ Loaded {len(projects)} projects")
        
        # Project selector
        if projects:
            project_names = [f"{p.get('org_name', 'Unknown')} - {p.get('asset_type', 'N/A')}" 
                           for p in projects]
            
            selected_idx = st.sidebar.selectbox(
                "Select Project",
                range(len(project_names)),
                format_func=lambda x: project_names[x]
            )
            
            st.session_state['selected_project'] = projects[selected_idx]
            st.sidebar.info(f"**Prompt:** {projects[selected_idx].get('prompt', '')[:50]}...")
    
    except Exception as e:
        st.sidebar.error(f"Error loading Excel: {e}")

# ==============================================
# MAIN TABS
# ==============================================
tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs([
    "🎯 Ideation Board", 
    "🖼️ Image Generator", 
    "🎬 Video Generator",
    "🧱 3D Creator",
    "📁 Library",
    "📜 History & Feedback",
    "🔄 Pipeline & Export"
])

# ==============================================
# TAB 1: IDEATION BOARD
# ==============================================
with tab1:
    st.header("🎯 Ideation & Prompt Board")
    st.markdown("**Brainstorm and refine prompts before generation**")
    
    col1, col2 = st.columns([2, 1])
    
    with col1:
        st.subheader("Prompt Ideas")
        
        # Preset templates for luxury real estate
        template = st.selectbox("Load Template", [
            "Custom",
            "Luxury Villa Exterior",
            "Penthouse Interior",
            "Aerial Property Tour",
            "3D Furniture Asset"
        ])
        
        if template == "Luxury Villa Exterior":
            idea_prompt = "Ultra-modern luxury villa at golden hour, infinity pool with crystal reflections, sleek glass facade, lush tropical landscaping, ocean view, photorealistic, architectural photography"
        elif template == "Penthouse Interior":
            idea_prompt = "Spacious penthouse interior, floor-to-ceiling windows, panoramic city skyline, designer furniture, marble floors, ambient lighting, wide-angle, luxury lifestyle"
        elif template == "Aerial Property Tour":
            idea_prompt = "Cinematic aerial drone footage of modern beachfront estate, golden hour lighting, smooth gimbal motion, reveal shot from ocean to property"
        elif template == "3D Furniture Asset":
            idea_prompt = "High-detail 3D model of contemporary outdoor lounge chair, teak wood frame, white weather-resistant cushions, for architectural visualization"
        else:
            idea_prompt = ""
        
        # Check if project is selected from Excel
        if 'selected_project' in st.session_state:
            proj = st.session_state['selected_project']
            idea_prompt = proj.get('prompt', idea_prompt)
            st.info(f"📋 Auto-filled from: {proj.get('org_name', 'Excel')}")
        
        idea_text = st.text_area("Prompt Concept", value=idea_prompt, height=150, key="idea_text")
        
        # PROMPT ENHANCER
        if idea_text and len(idea_text) > 10:
            st.markdown("### 🚀 Prompt Enhancer")
            
            # Helper to simulate enhancer if utils missing
            try:
                from utils.prompt_enhancer import enhance_prompt
                enhanced = enhance_prompt(idea_text)
            except ImportError:
                enhanced = {
                    "Cinematic": f"Cinematic shot of {idea_text}, 8k, highly detailed",
                    "Photorealistic": f"Photorealistic photo of {idea_text}, canon eos r5, 50mm",
                    "Artistic": f"Digital art of {idea_text}, trending on artstation"
                }
            
            enhanced_choice = st.radio(
                "Select Enhanced Version:",
                list(enhanced.keys()),
                key="idea_enhancer"
            )
            
            st.text_area(
                "Enhanced Prompt Preview",
                value=enhanced[enhanced_choice],
                height=100,
                disabled=True,
                key="idea_enhanced_preview"
            )
            
            st.session_state['ideation_final_prompt'] = enhanced[enhanced_choice]
        
        # Prompt versioning
        st.markdown("**Prompt Versions**")
        version = st.number_input("Version", min_value=1, value=1, step=1)
        
        if st.button("Save to Prompt Library"):
            st.success("Prompt saved to library with version tracking!")
    
    with col2:
        st.subheader("Prompt Strategy")
        st.markdown("""
        **Elements to Include:**
        - Subject/Scene
        - Lighting conditions
        - Camera angle
        - Style keywords
        - Quality modifiers
        
        **Best Practices:**
        - Be specific about materials
        - Mention atmosphere/mood
        - Include technical details
        - Specify use case
        """)
        
        st.markdown("---")
        st.info("💡 **Tip:** Test prompts with image generator first before scaling to video/3D")

# ==============================================
# TAB 2: IMAGE GENERATOR
# ==============================================
with tab2:
    st.header("🖼️ Image Generator")
    
    col1, col2 = st.columns([3, 1])
    
    with col1:
        selected_model = st.selectbox("Select Model", list(PROVIDERS.keys()), key="img_model")
        kind, model_name = PROVIDERS[selected_model]
        
        # Auto-fill from Excel if available
        default_prompt = "Ultra-modern luxury villa at sunset, glass façade, infinity pool, palm trees, ocean view, photorealistic, high detail"
        
        if 'selected_project' in st.session_state:
            proj = st.session_state['selected_project']
            if proj.get('asset_type', '').lower() in ['image', 'img']:
                default_prompt = proj.get('prompt', default_prompt)
                st.info(f"📋 Auto-filled from: {proj.get('org_name', 'Excel')}")
        
        prompt = st.text_area(
            "Image Prompt", 
            height=120, 
            value=default_prompt,
            key="img_prompt"
        )
        
        # PROMPT ENHANCER - MANDATORY
        final_prompt = prompt  # Default
        if prompt and len(prompt) > 10:
            st.markdown("### 🚀 Select Enhanced Prompt (Required)")
            
            try:
                from utils.prompt_enhancer import enhance_prompt
                enhanced = enhance_prompt(prompt)
            except ImportError:
                enhanced = {
                    "Cinematic": f"Cinematic shot of {prompt}, 8k, highly detailed",
                    "Photorealistic": f"Photorealistic photo of {prompt}, canon eos r5, 50mm",
                    "Artistic": f"Digital art of {prompt}, trending on artstation"
                }
            
            # Show all three options with their full text
            st.markdown("**Choose one enhancement:**")
            
            for idx, (title, enhanced_text) in enumerate(enhanced.items(), 1):
                st.markdown(f"**Option {idx}: {title}**")
                st.info(enhanced_text)
            
            enhanced_choice = st.radio(
                "Select your preferred version:",
                list(enhanced.keys()),
                key="img_enhancer",
                label_visibility="collapsed"
            )
            
            final_prompt = enhanced[enhanced_choice]
            
            st.success(f"✅ Selected: **{enhanced_choice}**")
        
        col_neg, col_num = st.columns(2)
        with col_neg:
            negative_prompt = st.text_input("Negative Prompt", value="low quality, blurry, distorted", key="img_neg")
        with col_num:
            n_images = st.slider("Number of Images", 1, 4, 2, key="img_count")
        
        # Advanced settings
        with st.expander("Advanced Settings"):
            col_adv1, col_adv2 = st.columns(2)
            with col_adv1:
                steps = st.slider("Inference Steps", 20, 50, 30)
                guidance = st.slider("Guidance Scale", 5.0, 15.0, 7.5, 0.5)
            with col_adv2:
                width = st.selectbox("Width", [512, 768, 1024], index=2)
                height = st.selectbox("Height", [512, 768, 1024], index=2)
        
        if st.button("🎨 Generate Images", type="primary", key="gen_img"):
            provider = get_provider(kind, model_name)
            
            try:
                with st.spinner(f"Generating images using {selected_model}..."):
                    outputs = provider.generate(
                        final_prompt, 
                        negative_prompt, 
                        n_images=n_images,
                        steps=steps,
                        guidance=guidance
                    )
                
                # Save generated images
                saved = []
                for img in outputs:
                    fname = f"img_{uuid.uuid4().hex[:8]}.png"
                    fpath = STORAGE_DIR / fname
                    img.save(fpath)
                    saved.append(str(fpath))
                
                # Save to history
                entry_id = save_to_history(
                    "image", final_prompt, negative_prompt, selected_model, model_name, 
                    saved, {"steps": steps, "guidance": guidance, "resolution": f"{width}x{height}"}
                )
                
                st.success(f"✅ Generated {len(saved)} images!")
                
                # Display results
                cols = st.columns(min(len(saved), 3))
                for idx, (c, path) in enumerate(zip(cols, saved)):
                    with c:
                        # FIXED: Changed use_container_width to use_column_width
                        c.image(path, use_column_width=True)
                        c.download_button(
                            "Download", 
                            data=open(path, "rb"), 
                            file_name=f"image_{idx+1}.png",
                            mime="image/png",
                            key=f"dl_img_{idx}"
                        )
            
            except Exception as e:
                st.error(f"❌ Generation failed: {e}")
    
    with col2:
        st.subheader("Quick Guide")
        st.markdown("""
        **Image Prompt Tips:**
        
        1. **Composition**
           - Camera angle
           - Framing
           - Rule of thirds
        
        2. **Lighting**
           - Time of day
           - Light direction
           - Atmosphere
        
        3. **Style**
           - Photorealistic
           - Architectural
           - Cinematic
        
        4. **Quality**
           - High detail
           - 8K resolution
           - Professional
        """)

# ==============================================
# TAB 3: VIDEO GENERATOR
# ==============================================
with tab3:
    st.header("🎬 Video Generator")
    st.info("🚧 Video generation pipeline - Currently in demo mode")
    
    col1, col2 = st.columns([3, 1])
    
    with col1:
        video_model = st.selectbox("Select Video Model", list(VIDEO_PROVIDERS.keys()))
        
        # Auto-fill from Excel if available
        default_video_prompt = "Aerial drone video fly-through of modern seaside estate, golden hour, lush landscaping, cinematic, smooth motion, 10 seconds"
        
        if 'selected_project' in st.session_state:
            proj = st.session_state['selected_project']
            if proj.get('asset_type', '').lower() == 'video':
                default_video_prompt = proj.get('prompt', default_video_prompt)
                st.info(f"📋 Auto-filled from: {proj.get('org_name', 'Excel')}")
        
        video_prompt = st.text_area(
            "Video Prompt",
            height=120,
            value=default_video_prompt,
            key="vid_prompt"
        )
        
        # PROMPT ENHANCER - MANDATORY
        final_video_prompt = video_prompt  # Default
        if video_prompt and len(video_prompt) > 10:
            st.markdown("### 🚀 Select Enhanced Prompt (Required)")
            
            try:
                from utils.prompt_enhancer import enhance_prompt
                enhanced = enhance_prompt(video_prompt)
            except ImportError:
                enhanced = {
                    "Cinematic Motion": f"{video_prompt}, cinematic camera movement, smooth stabilization",
                    "Dynamic Action": f"{video_prompt}, dynamic angle, fast paced",
                    "Slow Mo": f"{video_prompt}, slow motion, 60fps, detailed"
                }
            
            enhanced_choice = st.radio(
                "Choose one enhancement:",
                list(enhanced.keys()),
                key="vid_enhancer"
            )
            
            final_video_prompt = enhanced[enhanced_choice]
            
            st.text_area(
                "Final Prompt to Generate",
                value=final_video_prompt,
                height=80,
                disabled=True,
                key="vid_final_preview"
            )
        
        col_dur, col_fps = st.columns(2)
        with col_dur:
            duration = st.slider("Duration (seconds)", 5, 30, 10)
        with col_fps:
            fps = st.selectbox("FPS", [24, 30, 60], index=1)
        
        motion_intensity = st.slider("Motion Intensity", 1, 10, 5)
        
        if st.button("🎬 Generate Video", type="primary"):
            st.warning("Video generation requires RunwayML or Stable Video Diffusion API. Demo mode active.")
            st.info(f"**Would generate with prompt:** {final_video_prompt}")
    
    with col2:
        st.subheader("Video Strategy")
        st.markdown("""
        **Key Elements:**
        - Camera movement
        - Shot duration
        - Transitions
        - Pacing
        
        **Types:**
        - Property walkthrough
        - Aerial tour
        - Detail shots
        - Time-lapse
        """)

# ==============================================
# TAB 4: 3D CREATOR
# ==============================================
with tab4:
    st.header("🧱 3D Asset Creator")
    st.info("🚧 3D generation pipeline - Currently in demo mode")
    
    col1, col2 = st.columns([3, 1])
    
    with col1:
        model_3d = st.selectbox("Select 3D Model", list(MODEL_3D_PROVIDERS.keys()))
        
        asset_prompt = st.text_area(
            "3D Asset Prompt",
            height=120,
            value="High-detail 3D model of contemporary outdoor lounge chair, teak wood and white cushions, clean geometry, for architectural visualization",
            key="3d_prompt"
        )
        
        col_poly, col_tex = st.columns(2)
        with col_poly:
            poly_count = st.selectbox("Poly Count", ["Low (5K)", "Medium (20K)", "High (50K)"])
        with col_tex:
            texture_res = st.selectbox("Texture Resolution", ["1K", "2K", "4K"])
        
        export_format = st.multiselect(
            "Export Formats",
            ["FBX", "OBJ", "GLTF", "USD", "Blender (.blend)"],
            default=["FBX", "Blender (.blend)"]
        )
        
        if st.button("🧱 Generate 3D Asset", type="primary"):
            st.warning("3D generation requires Shap-E or similar 3D generation API. Demo mode active.")
            with st.spinner("Simulating 3D generation..."):
                import time
                time.sleep(2)
            
            st.success("✅ 3D asset generation placeholder complete!")
            st.info("**Blender Integration:** Assets can be exported to `.blend` format for direct import")
    
    with col2:
        st.subheader("3D Workflow")
        st.markdown("""
        **Pipeline:**
        1. Generate base mesh
        2. Apply textures
        3. Optimize topology
        4. Export to Blender
        5. Final rendering
        
        **Blender Integration:**
        - Direct .blend export
        - Material nodes
        - Scene composition
        """)

# ==============================================
# TAB 5: LIBRARY
# ==============================================
with tab5:
    st.header("📁 Project Library & Asset Management")
    
    # Filter options
    col_f1, col_f2, col_f3 = st.columns(3)
    with col_f1:
        filter_type = st.selectbox("Asset Type", ["All", "Images", "Videos", "3D Models"])
    with col_f2:
        sort_by = st.selectbox("Sort By", ["Newest First", "Oldest First", "Most Feedback"])
    with col_f3:
        st.write("")  # Spacer
        show_grid = st.checkbox("Grid View", value=True)
    
    files = sorted(STORAGE_DIR.glob("*.png"), reverse=True)
    
    if not files:
        st.info("No generated assets yet. Start creating in the generator tabs!")
    else:
        if show_grid:
            # Grid layout
            cols_per_row = 3
            for i in range(0, len(files), cols_per_row):
                cols = st.columns(cols_per_row)
                for idx, f in enumerate(files[i:i+cols_per_row]):
                    with cols[idx]:
                        # FIXED: Changed use_container_width to use_column_width
                        st.image(str(f), use_column_width=True)
                        st.caption(f.name)
                        col_dl, col_fb = st.columns(2)
                        with col_dl:
                            st.download_button(
                                "⬇️", 
                                data=open(f, "rb"), 
                                file_name=f.name,
                                mime="image/png",
                                key=f"lib_dl_{f.name}"
                            )
                        with col_fb:
                            if st.button("💬", key=f"lib_fb_{f.name}"):
                                st.info("Feedback panel would open here")
        else:
            # List layout
            for f in files:
                col1, col2, col3 = st.columns([3, 1, 1])
                with col1:
                    # FIXED: Changed width parameter logic for compatibility
                    st.image(str(f), width=300)
                with col2:
                    st.write(f"**{f.name}**")
                    st.caption(f"Size: {f.stat().st_size // 1024}KB")
                with col3:
                    st.download_button(
                        "Download",
                        data=open(f, "rb"),
                        file_name=f.name,
                        mime="image/png",
                        key=f"list_dl_{f.name}"
                    )

# ==============================================
# TAB 6: HISTORY & FEEDBACK
# ==============================================
with tab6:
    st.header("📜 Prompt History & Feedback System")
    
    try:
        history = json.loads(PROMPT_HISTORY.read_text())
    except json.JSONDecodeError:
        history = []
    
    if not history:
        st.info("No generation history yet.")
    else:
        for item in history:
            # Handle both old and new format
            asset_type = item.get('asset_type', 'image').upper()
            with st.expander(f"{asset_type}: {item['prompt'][:80]}...", expanded=False):
                col1, col2 = st.columns([2, 1])
                
                with col1:
                    st.markdown(f"**Prompt:** {item['prompt']}")
                    st.caption(f"Provider: {item['provider']} | {item['timestamp']}")
                    
                    if item.get("negative_prompt"):
                        st.markdown(f"**Negative:** {item['negative_prompt']}")
                    
                    # Show results
                    if item["results"]:
                        result_cols = st.columns(min(len(item['results']), 3))
                        for idx, (col, path) in enumerate(zip(result_cols, item['results'])):
                            if Path(path).exists():
                                # FIXED: Changed use_container_width to use_column_width
                                col.image(path, use_column_width=True)
                
                with col2:
                    st.markdown("**Feedback & Iteration**")
                    
                    # Add feedback
                    rating = st.select_slider(
                        "Quality Rating",
                        options=[1, 2, 3, 4, 5],
                        value=3,
                        key=f"rating_{item['id']}"
                    )
                    
                    feedback_text = st.text_area(
                        "Feedback Notes",
                        placeholder="What would improve this?",
                        key=f"feedback_{item['id']}",
                        height=100
                    )
                    
                    if st.button("Submit Feedback", key=f"submit_{item['id']}"):
                        add_feedback(item['id'], feedback_text, rating)
                        st.success("Feedback saved!")
                    
                    # Show existing feedback
                    if item.get("feedback"):
                        st.markdown("**Previous Feedback:**")
                        for fb in item["feedback"]:
                            st.caption(f"⭐ {fb['rating']}/5: {fb['text']}")
                
                st.markdown("---")

# ==============================================
# TAB 7: PIPELINE & EXPORT
# ==============================================
with tab7:
    st.header("🔄 Integration Pipeline & Export")
    
    st.markdown("""
    ## Workflow: Concept to Campaign-Ready Asset
    
    This pipeline shows how assets flow from ideation to final delivery:
    """)
    
    # Visual workflow
    st.markdown("""
    ```
    1. Creative Brief Submission
           ↓
    2. Ideation Board → Prompt Refinement → Version Control
           ↓
    3. Asset Generation (Image/Video/3D)
           ↓
    4. Team Review & Feedback Loop
           ↓
    5. Iteration & Refinement
           ↓
    6. Export & Integration:
       • Images → Marketing materials
       • 3D Assets → Blender for rendering
       • 3D Assets → Unity for interactive tours
       • Videos → Campaign distribution
           ↓
    7. Final Review & Campaign Launch
    ```
    """)
    
    st.markdown("---")
    
    # Export options
    st.subheader("Export & Integration Tools")
    
    col1, col2 = st.columns(2)
    
    with col1:
        st.markdown("### Blender Integration")
        st.code("""
# Python script for Blender export
import bpy

def export_to_blender(asset_path):
    bpy.ops.import_scene.fbx(filepath=asset_path)
    # Set up materials, lighting
    bpy.ops.wm.save_as_mainfile(
        filepath="scene_output.blend"
    )
        """, language="python")
        
        if st.button("Generate Blender Export Script"):
            st.download_button(
                "Download Script",
                data="""import bpy\n# Your export script""",
                file_name="blender_export.py",
                mime="text/plain"
            )
    
    with col2:
        st.markdown("### Unity Integration")
        st.code("""
// C# script for Unity import
using UnityEngine;

public class AssetImporter : MonoBehaviour
{
    void ImportAsset(string path) {
        GameObject asset = 
            AssetDatabase.LoadAssetAtPath<GameObject>(path);
        Instantiate(asset);
    }
}
        """, language="csharp")
        
        if st.button("Generate Unity Script"):
            st.download_button(
                "Download Script",
                data="""using UnityEngine;\n// Your import script""",
                file_name="AssetImporter.cs",
                mime="text/plain"
            )
    
    st.markdown("---")
    
    # API Integration pseudocode
    st.subheader("API Workflow (Pseudocode)")
    st.code("""
def generate_campaign_asset(brief, prompt_type, iterations=3):
    \"\"\"
    Complete workflow from brief to final asset
    \"\"\"
    # Step 1: Create prompt from brief
    prompt = create_prompt(brief, prompt_type)
    
    # Step 2: Generate initial asset
    ai_output = call_ai_tool(prompt)
    
    # Step 3: Iteration loop with feedback
    for i in range(iterations):
        feedback = get_team_feedback(ai_output)
        if feedback['approved']:
            break
        prompt = refine_prompt(prompt, feedback)
        ai_output = call_ai_tool(prompt)
    
    # Step 4: Export based on asset type
    if prompt_type == "3D":
        exported = import_to_blender(ai_output)
    elif prompt_type == "video":
        exported = save_to_library(ai_output)
    elif prompt_type == "image":
        exported = optimize_for_web(ai_output)
    
    # Step 5: Save to project
    save_to_project(brief['project_id'], exported)
    
    return exported

# Example usage
brief = {
    'project_id': 'luxury_realestate_q4',
    'description': 'High-end villa marketing campaign',
    'target_audience': 'Ultra-high-net-worth individuals',
    'style': 'Photorealistic, cinematic'
}

image = generate_campaign_asset(brief, 'image')
video = generate_campaign_asset(brief, 'video')
model_3d = generate_campaign_asset(brief, '3D')
    """, language="python")
    
    st.markdown("---")
    st.success("✅ Complete workflow pipeline ready for presentation!")

# ==============================================
# FOOTER
# ==============================================
st.sidebar.markdown("---")
st.sidebar.markdown("### 📊 System Status")
st.sidebar.success("✅ Image Generation: Active")
st.sidebar.warning("⚠️ Video Generation: Demo Mode")
st.sidebar.warning("⚠️ 3D Generation: Demo Mode")
st.sidebar.info("💡 Tip: Complete image pipeline first, then add video/3D APIs")