full_pipeline.py

"""

Full Pipeline: Prompt → Manifest → Images → Selection → Composition

Orchestrates the complete workflow from user prompt to final MP4 video

"""

import requests
import json
import os
from pathlib import Path
from PIL import Image
from io import BytesIO
import asyncio
import subprocess
import sys
from datetime import datetime


# ─────────────────────────────────────────────────────────────────────────
# Configuration
# ─────────────────────────────────────────────────────────────────────────

MANIFEST_SERVER = "https://factorstudios-content-gen.hf.space"
IMAGE_SERVER = "https://factorstudios-pinteresting.hf.space"
PIPELINE_DIR = Path(__file__).parent
CANDIDATES_DIR = PIPELINE_DIR / "candidates"
SELECTED_DIR = PIPELINE_DIR / "selected"
RENDERS_DIR = PIPELINE_DIR / "renders"


# ─────────────────────────────────────────────────────────────────────────
# Step 1: Generate Manifest from Prompt
# ─────────────────────────────────────────────────────────────────────────

async def step_generate_manifest(prompt: str, output_dir: Path = PIPELINE_DIR) -> dict:
    """

    Call content-gen server to generate manifest from prompt.

    Saves manifest to manifest_response.json

    

    Args:

        prompt (str): User prompt describing video content

        output_dir (Path): Directory to save manifest

        

    Returns:

        dict: Manifest with title and scenes

    """
    print("\n" + "="*70)
    print(f"[STEP 1] Generating Manifest from Prompt")
    print("="*70)
    print(f"Prompt: {prompt[:80]}...")
    
    try:
        # Call manifest generation server
        payload = {"prompt": prompt}
        print(f"Calling {MANIFEST_SERVER}/generate...")
        
        response = requests.post(
            f"{MANIFEST_SERVER}/generate",
            json=payload,
            timeout=60
        )
        response.raise_for_status()
        manifest = response.json()
        
        # Save manifest to file
        manifest_path = output_dir / "manifest_response.json"
        with open(manifest_path, "w") as f:
            json.dump(manifest, f, indent=2)
        
        scenes = manifest.get("scenes", [])
        print(f"✓ Generated manifest with {len(scenes)} scenes")
        print(f"✓ Saved to {manifest_path.name}")
        
        # Print scene details
        for idx, scene in enumerate(scenes):
            label = scene.get("label", f"Scene {idx}")
            query = scene.get("image_query", "")
            print(f"  Scene {idx}: {label} (query: '{query[:30]}...')")
        
        return manifest
        
    except Exception as e:
        print(f"✗ Failed to generate manifest: {e}")
        raise


# ─────────────────────────────────────────────────────────────────────────
# Step 2: Download Images for Each Scene
# ─────────────────────────────────────────────────────────────────────────

async def step_download_images(

    manifest: dict,

    output_dir: Path = CANDIDATES_DIR,

    images_per_scene: int = 5

) -> int:
    """

    Download images from pinteresting server for each scene in manifest.

    IMPORTANT: Downloads image for TITLE (scene 0) + all scenes in manifest.scenes

    Follows the pattern from test_api.py

    

    Args:

        manifest (dict): Manifest with title and scenes

        output_dir (Path): Base directory to organize images

        images_per_scene (int): Number of images per scene

        

    Returns:

        int: Total number of images downloaded

    """
    print("\n" + "="*70)
    print(f"[STEP 2] Downloading Images (Title + Scenes)")
    print("="*70)
    
    # Clear and recreate candidates directory
    if output_dir.exists():
        import shutil
        shutil.rmtree(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    
    total_downloaded = 0
    
    # STEP 2.0: Download image for TITLE (becomes scene_0)
    title = manifest.get("title", "")
    if title:
        scene_dir = output_dir / "scene_0"
        scene_dir.mkdir(parents=True, exist_ok=True)
        
        print(f"\n[Scene 0] {title} (TITLE/INTRO)")
        print(f"  Query: {title}")
        
        try:
            payload = {
                "keyword": title,
                "count": images_per_scene
            }
            
            print(f"  Calling {IMAGE_SERVER}/scrape...")
            response = requests.post(
                f"{IMAGE_SERVER}/scrape",
                json=payload,
                timeout=60
            )
            response.raise_for_status()
            data = response.json()
            images = data.get("images", [])
            
            print(f"  Downloaded {len(images)} images")
            
            for img_idx, img_url in enumerate(images):
                try:
                    img_response = requests.get(img_url, timeout=30)
                    if img_response.status_code == 200:
                        img_path = scene_dir / f"candidate_{img_idx:02d}.jpg"
                        with open(img_path, "wb") as f:
                            f.write(img_response.content)
                        total_downloaded += 1
                except Exception as e:
                    print(f"    ⚠  Failed to save image {img_idx}: {e}")
                    
        except Exception as e:
            print(f"  ⚠  Error downloading images for title: {e}")
    
    # STEP 2.1: Download images for each content scene (becomes scene_1, scene_2, etc)
    scenes = manifest.get("scenes", [])
    
    for scene_idx, scene in enumerate(scenes):
        actual_idx = scene_idx + 1  # scene_1, scene_2, etc (title is scene_0)
        scene_label = scene.get("label", f"Scene {actual_idx}")
        image_query = scene.get("image_query", "")
        
        if not image_query:
            print(f"\n[Scene {actual_idx}] ⚠  No image query found, skipping...")
            continue
        
        # Create scene-specific folder
        scene_dir = output_dir / f"scene_{actual_idx}"
        scene_dir.mkdir(parents=True, exist_ok=True)
        
        print(f"\n[Scene {actual_idx}] {scene_label}")
        print(f"  Query: {image_query}")
        
        # Fetch images from pinteresting API
        try:
            payload = {
                "keyword": image_query,
                "count": images_per_scene
            }
            
            print(f"  Calling {IMAGE_SERVER}/scrape...")
            response = requests.post(
                f"{IMAGE_SERVER}/scrape",
                json=payload,
                timeout=60
            )
            response.raise_for_status()
            data = response.json()
            
            if data.get("success"):
                images = data.get("images", [])
                print(f"  ✓ Found {len(images)} images")
                
                # Download each image
                for img_idx, img_data in enumerate(images):
                    img_url = img_data.get("url")
                    if not img_url:
                        continue
                    
                    try:
                        # Download image
                        img_response = requests.get(img_url, timeout=15)
                        img_response.raise_for_status()
                        
                        # Verify it's a valid image
                        img = Image.open(BytesIO(img_response.content))
                        
                        # Save image
                        file_name = f"candidate_{img_idx:02d}.jpg"
                        file_path = scene_dir / file_name
                        
                        with open(file_path, "wb") as f:
                            f.write(img_response.content)
                        
                        size_kb = len(img_response.content) / 1024
                        dims = f"{img_data.get('width', '?')}x{img_data.get('height', '?')}"
                        print(f"    ✓ {file_name} ({dims}, {size_kb:.0f}KB)")
                        total_downloaded += 1
                        
                    except Exception as e:
                        print(f"    ✗ Image {img_idx} failed: {e}")
            else:
                print(f"  ✗ API Error: {data.get('message')}")
                
        except Exception as e:
            print(f"  ✗ Request failed: {e}")
    
    print(f"\n✓ Downloaded {total_downloaded} images total")
    return total_downloaded


# ─────────────────────────────────────────────────────────────────────────
# Step 3: Select Best Image from Each Scene's Candidates
# ─────────────────────────────────────────────────────────────────────────

async def step_select_scenes(manifest: dict, candidates_dir: Path = CANDIDATES_DIR) -> dict:
    """

    Select best image from each scene's candidate folder.

    IMPORTANT: Selects from TITLE (scene_0) + all scenes in manifest.scenes

    Evaluates by file size (largest = best quality).

    

    Args:

        manifest (dict): Manifest with scene count

        candidates_dir (Path): Directory with candidate images

        

    Returns:

        dict: Selection results

    """
    print("\n" + "="*70)
    print(f"[STEP 3] Selecting Best Images from Candidates")
    print("="*70)
    
    # Ensure selected directory exists
    SELECTED_DIR.mkdir(parents=True, exist_ok=True)
    
    scenes = manifest.get("scenes", [])
    selected_count = 0
    
    # Select from scene_0 (title) through scene_N (content scenes)
    # Total scenes = len(scenes) + 1 (for title as scene_0)
    total_scene_count = len(scenes) + 1
    
    for scene_idx in range(total_scene_count):
        scene_folder = candidates_dir / f"scene_{scene_idx}"
        
        if not scene_folder.exists():
            if scene_idx == 0:
                print(f"[Scene {scene_idx}] ✗ No candidates found (TITLE)")
            else:
                print(f"[Scene {scene_idx}] ✗ No candidates found")
            continue
        
        # Find largest image (best quality)
        images = list(scene_folder.glob("*.jpg"))
        if not images:
            if scene_idx == 0:
                print(f"[Scene {scene_idx}] ✗ No JPEG images found (TITLE)")
            else:
                print(f"[Scene {scene_idx}] ✗ No JPEG images found")
            continue
        
        best_img = max(images, key=lambda p: p.stat().st_size)
        size_kb = best_img.stat().st_size / 1024
        
        # Copy to selected folder
        selected_path = SELECTED_DIR / f"scene_{scene_idx:02d}.jpg"
        import shutil
        shutil.copy2(best_img, selected_path)
        
        if scene_idx == 0:
            print(f"[Scene {scene_idx}] ✓ Selected {best_img.name} ({size_kb:.0f}KB) [TITLE]")
        else:
            print(f"[Scene {scene_idx}] ✓ Selected {best_img.name} ({size_kb:.0f}KB)")
        selected_count += 1
    
    print(f"\n✓ Selected {selected_count} images ({total_scene_count} total: title + {len(scenes)} scenes)")
    
    return {
        "status": "success",
        "selected": selected_count,
        "total": total_scene_count
    }


# ─────────────────────────────────────────────────────────────────────────
# Step 4: Compose Video with Selected Images and Manifest
# ─────────────────────────────────────────────────────────────────────────

async def step_compose_video(manifest: dict) -> dict:
    """

    Compose final video using selected images and manifest labels.

    Calls the FastAPI /compose endpoint which handles scene config generation.

    

    Args:

        manifest (dict): Manifest with title and scenes

        

    Returns:

        dict: Composition results with video path and metadata

    """
    print("\n" + "="*70)
    print(f"[STEP 4] Composing Video from Selected Images")
    print("="*70)
    
    scenes = manifest.get("scenes", [])
    selected_images = sorted(SELECTED_DIR.glob("scene_*.jpg"))
    
    print(f"Manifest title: {manifest.get('title', 'Untitled')}")
    print(f"Selected images: {len(selected_images)}")
    print(f"Required images: {len(scenes) + 1} (title + {len(scenes)} scenes)")
    
    # Expected: title + all scenes
    expected_images = len(scenes) + 1
    if len(selected_images) != expected_images:
        raise Exception(
            f"Image count mismatch: expected {expected_images}, "
            f"found {len(selected_images)}"
        )
    
    # Call the FastAPI /compose endpoint
    print(f"\nCalling /compose endpoint...")
    
    try:
        payload = {
            "title": manifest.get("title", "Untitled"),
            "scenes": [
                {
                    "label": s.get("label", f"Scene {idx}"),
                    "image_query": s.get("image_query", "")
                }
                for idx, s in enumerate(scenes)
            ]
        }
        
        response = requests.post(
            f"http://localhost:7860/compose",
            json=payload,
            timeout=300
        )
        
        if response.status_code != 200:
            error_data = response.json() if response.headers.get("content-type") == "application/json" else response.text
            print(f"✗ Server returned {response.status_code}: {error_data}")
            raise Exception(f"Compose endpoint failed: {error_data}")
        
        # Check if response is binary (video file) or JSON
        if response.headers.get("content-type", "").startswith("video"):
            # Save video file
            output_path = PIPELINE_DIR / "output_video.mp4"
            with open(output_path, "wb") as f:
                f.write(response.content)
            
            size_mb = output_path.stat().st_size / (1024 * 1024)
            print(f"✓ Video saved: {output_path.name} ({size_mb:.2f}MB)")
            
            return {
                "status": "success",
                "video_path": str(output_path),
                "size_mb": size_mb,
                "scenes": len(scenes) + 1
            }
        else:
            # Response is JSON (might be error or status)
            data = response.json()
            if data.get("status") == "success":
                print(f"✓ Compose completed successfully")
                return data
            else:
                raise Exception(f"Compose failed: {data.get('message', 'Unknown error')}")
    
    except Exception as e:
        print(f"✗ Composition failed: {e}")
        raise
    
    # Generate dynamic SCENE_CONFIG from manifest
    print(f"\nGenerating scene configuration...")
    
    try:
        payload = {
            "title": manifest.get("title", "Untitled"),
            "scenes": [
                {
                    "label": s.get("label", f"Scene {idx}"),
                    "image_query": s.get("image_query", "")
                }
                for idx, s in enumerate(scenes)
            ]
        }
        
        response = requests.post(
            f"http://localhost:7860/compose",
            json=payload,
            timeout=300
        )
        
        if response.status_code != 200:
            error_data = response.json() if response.headers.get("content-type") == "application/json" else response.text
            print(f"✗ Server returned {response.status_code}: {error_data}")
            raise Exception(f"Compose endpoint failed: {error_data}")
        
        # Check if response is binary (video file) or JSON
        if response.headers.get("content-type", "").startswith("video"):
            # Save video file
            output_path = PIPELINE_DIR / "output_video.mp4"
            with open(output_path, "wb") as f:
                f.write(response.content)
            
            size_mb = output_path.stat().st_size / (1024 * 1024)
            print(f"✓ Video saved: {output_path.name} ({size_mb:.2f}MB)")
            
            return {
                "status": "success",
                "video_path": str(output_path),
                "size_mb": size_mb,
                "scenes": len(scenes) + 1
            }
        else:
            # Response is JSON (might be error or status)
            data = response.json()
            if data.get("status") == "success":
                print(f"✓ Compose completed successfully")
                return data
            else:
                raise Exception(f"Compose failed: {data.get('message', 'Unknown error')}")
    
    except Exception as e:
        print(f"✗ Composition failed: {e}")
        raise


# ─────────────────────────────────────────────────────────────────────────
# Main Pipeline Orchestrator
# ─────────────────────────────────────────────────────────────────────────

async def generate_video_from_prompt(prompt: str) -> dict:
    """

    Complete pipeline: Prompt → Manifest → Images → Selection → Video

    

    Args:

        prompt (str): User prompt describing video content

        

    Returns:

        dict: Final result with video path or error

    """
    try:
        # Step 1: Generate manifest from prompt
        manifest = await step_generate_manifest(prompt)
        
        # Step 2: Download images for each scene
        downloaded = await step_download_images(manifest)
        if downloaded == 0:
            raise Exception("No images were downloaded")
        
        # Step 3: Select best images from candidates
        selection = await step_select_scenes(manifest)
        if selection["selected"] != selection["total"]:
            raise Exception(
                f"Selection incomplete: {selection['selected']}/{selection['total']}"
            )
        
        # Step 4: Compose final video
        composition = await step_compose_video(manifest)
        
        # Success!
        print("\n" + "="*70)
        print("[SUCCESS] Pipeline Complete!")
        print("="*70)
        print(f"Title: {manifest.get('title', 'Untitled')}")
        print(f"Scenes: {len(manifest.get('scenes', []))}")
        print(f"Video: {composition['output_path']}")
        print(f"Size: {composition['size_mb']:.1f}MB")
        print("="*70)
        
        return {
            "status": "success",
            "message": "Video generated successfully",
            "title": manifest.get("title"),
            "scenes": len(manifest.get("scenes", [])),
            "output_path": composition["output_path"],
            "size_mb": composition["size_mb"],
        }
        
    except Exception as e:
        print("\n" + "="*70)
        print(f"[ERROR] Pipeline Failed: {e}")
        print("="*70)
        
        return {
            "status": "error",
            "message": str(e),
            "output_path": None,
        }


# ─────────────────────────────────────────────────────────────────────────
# Local Testing
# ─────────────────────────────────────────────────────────────────────────

if __name__ == "__main__":
    import sys
    
    if len(sys.argv) > 1:
        prompt = " ".join(sys.argv[1:])
    else:
        prompt = "A motivational video about personal growth and success"
    
    # Ensure directories exist
    PIPELINE_DIR.mkdir(exist_ok=True)
    RENDERS_DIR.mkdir(exist_ok=True)
    
    # Run pipeline
    result = asyncio.run(generate_video_from_prompt(prompt))
    
    # Print final status
    if result["status"] == "success":
        print(f"\n✓ Video saved to: {result['output_path']}")
        sys.exit(0)
    else:
        print(f"\n✗ Error: {result['message']}")
        sys.exit(1)