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Fact Creator Service
Main orchestrator for generating fact-image videos
"""
import asyncio
import logging
import uuid
import time
from pathlib import Path
from typing import Dict, List, Optional
from datetime import datetime
from ..schemas import JobStatus, ImageModel
from .text_overlay import TextOverlay
logger = logging.getLogger(__name__)
# Master prompt for image generation (CLEAN, NO TEXT)
IMAGE_SYSTEM_PROMPT = """Generate a clean, cinematic, vertical image (9:16 aspect ratio) based on this description:
{image_prompt}
IMPORTANT:
- Do NOT add any text, words, letters, numbers, or watermarks in the image
- Leave empty/soft area at bottom 40% for text overlay
- Clean aesthetic, modern TikTok/Instagram style
- High-quality, beautiful lighting"""
class FactCreator:
"""
Main orchestrator for fact-image video generation.
Pipeline:
1. Generate clean image (NVIDIA/Cloudflare/Pexels)
2. Add text overlay (PIL)
3. Create short video (MoviePy)
4. Upload to cloud (optional)
"""
# Video settings
TARGET_WIDTH = 1080
TARGET_HEIGHT = 1920
FPS = 24
FADE_DURATION = 0.3
def __init__(
self,
config,
nvidia_client=None,
cloudflare_client=None,
pexels_client=None
):
self.config = config
self.nvidia = nvidia_client
self.cloudflare = cloudflare_client
self.pexels = pexels_client
self.text_overlay = TextOverlay()
# Job tracking
self.jobs: Dict[str, Dict] = {}
self.queue: List[Dict] = []
self.processing = False
def add_to_queue(
self,
model: ImageModel,
image_prompt: str,
fact_text: str,
duration: int = 5,
fact_heading: str = None,
heading_background: dict = None
) -> str:
"""
Add fact-image job to queue.
Returns:
job_id for tracking
"""
job_id = str(uuid.uuid4()).replace('-', '')[:16]
job = {
"id": job_id,
"model": model,
"image_prompt": image_prompt,
"fact_heading": fact_heading,
"heading_background": heading_background,
"fact_text": fact_text,
"duration": duration,
"status": JobStatus.queued,
"progress": 0,
"created_at": datetime.now().isoformat(),
"video_url": None,
"error": None
}
self.jobs[job_id] = job
self.queue.append(job)
logger.info(f"Added job {job_id} to queue. Queue length: {len(self.queue)}")
# Start processing if not already running
if not self.processing:
asyncio.create_task(self.process_queue())
return job_id
async def process_queue(self):
"""Process jobs in queue"""
if self.processing:
return
self.processing = True
try:
while self.queue:
job = self.queue[0]
job_id = job["id"]
logger.info(f"Processing job {job_id}")
try:
await self._process_job(job)
job["status"] = JobStatus.ready
job["progress"] = 100
logger.info(f"Job {job_id} completed successfully")
except Exception as e:
logger.error(f"Job {job_id} failed: {e}", exc_info=True)
job["status"] = JobStatus.failed
job["error"] = str(e)
finally:
self.queue.pop(0)
finally:
self.processing = False
async def _process_job(self, job: Dict):
"""Process a single fact-image job"""
job_id = job["id"]
temp_dir = self.config.temp_dir_path / job_id
temp_dir.mkdir(parents=True, exist_ok=True)
try:
# ====================
# Step 1: Generate Image
# ====================
job["status"] = JobStatus.generating_image
job["progress"] = 10
logger.info(f"[{job_id}] Generating image with {job['model']}...")
image_path = temp_dir / "base_image.png"
# Build full prompt
full_prompt = IMAGE_SYSTEM_PROMPT.format(image_prompt=job["image_prompt"])
if job["model"] == ImageModel.nvidia and self.nvidia:
# NVIDIA: uses aspect_ratio "9:16" internally (no width/height params)
self.nvidia.generate_and_save(
prompt=full_prompt,
output_path=image_path
)
elif job["model"] == ImageModel.cloudflare and self.cloudflare:
# Cloudflare: supports width/height (1080x1920)
self.cloudflare.generate_and_save(
prompt=full_prompt,
output_path=image_path,
width=1080,
height=1920
)
elif job["model"] == ImageModel.pexels:
# Pexels: Direct API call to get FIRST (most relevant) photo
import requests as pexels_requests
import os
pexels_key = os.getenv("PEXELS_API_KEY")
if not pexels_key:
raise Exception("PEXELS_API_KEY not configured")
logger.info(f"[{job_id}] Searching Pexels for: {job['image_prompt']}")
resp = pexels_requests.get(
"https://api.pexels.com/v1/search",
headers={"Authorization": pexels_key},
params={
"query": job["image_prompt"],
"orientation": "portrait",
"per_page": 5,
"size": "large"
},
timeout=15
)
if resp.status_code != 200:
raise Exception(f"Pexels API error: {resp.status_code}")
photos = resp.json().get("photos", [])
if not photos:
raise Exception(f"No Pexels photos found for: {job['image_prompt']}")
# Select FIRST (most relevant) photo, NOT random
photo = photos[0]
photo_url = photo.get("src", {}).get("original") or photo.get("src", {}).get("large")
if not photo_url:
raise Exception("No valid photo URL from Pexels")
logger.info(f"[{job_id}] Selected Pexels photo ID {photo['id']}")
# Download photo
img_resp = pexels_requests.get(photo_url, timeout=30)
img_resp.raise_for_status()
image_path.parent.mkdir(parents=True, exist_ok=True)
image_path.write_bytes(img_resp.content)
logger.info(f"[{job_id}] Downloaded Pexels photo")
else:
# Fallback to any available client
if self.nvidia:
self.nvidia.generate_and_save(full_prompt, image_path)
elif self.cloudflare:
self.cloudflare.generate_and_save(full_prompt, image_path, width=1080, height=1920)
else:
raise Exception("No image generation client available!")
job["progress"] = 40
# ====================
# Step 2: Add Text Overlay
# ====================
job["status"] = JobStatus.adding_text
logger.info(f"[{job_id}] Adding text overlay...")
overlay_path = temp_dir / "overlay_image.png"
self.text_overlay.add_text(
image_path=image_path,
text=job["fact_text"],
output_path=overlay_path,
heading=job.get("fact_heading"),
heading_background=job.get("heading_background")
)
job["progress"] = 60
# ====================
# Step 3: Create Video
# ====================
job["status"] = JobStatus.creating_video
logger.info(f"[{job_id}] Creating {job['duration']}s video...")
output_path = self.config.videos_dir_path / f"{job_id}.mp4"
await self._create_video(overlay_path, output_path, job["duration"])
job["video_url"] = str(output_path)
job["progress"] = 90
# ====================
# Step 4: Upload to Cloud (Optional)
# ====================
from modules.shared.services.hf_storage import get_hf_storage
hf_client = get_hf_storage()
if hf_client and hf_client.enabled:
logger.info(f"[{job_id}] Uploading to HF Hub...")
cloud_url = hf_client.upload_video(output_path, job_id, "fact_image")
if cloud_url:
job["video_url"] = cloud_url
job["storage"] = "cloud"
# Save cloud URL to metadata file
cloud_file = output_path.with_suffix('.cloud')
cloud_file.write_text(cloud_url)
# Delete local file
output_path.unlink()
logger.info(f"[{job_id}] Uploaded to cloud, local file deleted")
logger.info(f"[{job_id}] Video ready: {job['video_url']}")
finally:
# Cleanup temp files
import shutil
if temp_dir.exists():
shutil.rmtree(temp_dir, ignore_errors=True)
async def _create_video(self, image_path: Path, output_path: Path, duration: int):
"""Create video from single image with fade effects (9:16 = 1080x1920)"""
from moviepy.editor import ImageClip
# Create image clip
clip = ImageClip(str(image_path)).set_duration(duration)
# Ensure 9:16 aspect ratio (1080x1920)
clip = clip.resize((self.TARGET_WIDTH, self.TARGET_HEIGHT))
# Add fade in/out
clip = clip.fadein(self.FADE_DURATION)
clip = clip.fadeout(self.FADE_DURATION)
# Write video
logger.info(f"Writing video: {duration}s, fade in/out {self.FADE_DURATION}s")
clip.write_videofile(
str(output_path),
fps=self.FPS,
codec='libx264',
audio=False, # No audio for fact videos
preset='ultrafast', # Fast export (3-5x faster)
ffmpeg_params=[
'-pix_fmt', 'yuv420p',
'-movflags', '+faststart',
'-profile:v', 'baseline',
'-level', '3.0'
]
)
clip.close()
def get_status(self, job_id: str) -> Dict:
"""Get job status"""
job = self.jobs.get(job_id)
if not job:
# Check for .cloud file (cloud-stored video)
cloud_file = self.config.videos_dir_path / f"{job_id}.cloud"
if cloud_file.exists():
return {
"job_id": job_id,
"status": JobStatus.ready,
"progress": 100,
"video_url": cloud_file.read_text().strip()
}
return {
"job_id": job_id,
"status": JobStatus.failed,
"progress": 0,
"error": "Job not found"
}
return {
"job_id": job["id"],
"status": job["status"],
"progress": job.get("progress", 0),
"video_url": job.get("video_url"),
"error": job.get("error")
}
def get_video_path(self, job_id: str) -> Optional[Path]:
"""Get video file path"""
return self.config.videos_dir_path / f"{job_id}.mp4"
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