README.md

import time from fastapi import FastAPI, HTTPException from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel, Field

app = FastAPI(title="OX AI Engine Core")

Configure CORS for local frontend testing

app.add_middleware( CORSMiddleware, allow_origins=[""], allow_credentials=True, allow_methods=[""], allow_headers=["*"], )

Mock in-memory user tracking database

USER_WALLET = {"available_credits": 500}

class VideoGenerationRequest(BaseModel): prompt: str = Field(..., example="Cinematic tracking shot of an astronaut navigating flowing neon rivers.") aspect_ratio: str = Field("16:9", example="16:9") resolution: str = Field("1080p", example="1080p") character_token: str = Field(None, description="Maintains face/asset identity signatures across generations") motion_intensity: int = Field(5, ge=1, le=10) native_audio: bool = Field(False)

@app.post("/api/generate-video") async def process_video_generation(request: VideoGenerationRequest): if not request.prompt.strip(): raise HTTPException(status_code=400, detail="Generation prompt cannot be empty.")

# Calculate explicit cost matrices based on operational footprint
base_cost = 10 
resolution_multiplier = 2.5 if request.resolution == "4k" else 1.0
audio_addon = 5 if request.native_audio else 0
total_cost = int((base_cost * resolution_multiplier) + audio_addon)

if USER_WALLET["available_credits"] < total_cost:
    return {"success": False, "message": "Insufficient rendering credits inside user profile."}

# Deduct credits transparently
USER_WALLET["available_credits"] -= total_cost

# Simulated AI Model Generation Time Pipeline (Diffusion step calculations)
# Inside a live environment, you would call requests.post("https://openai.com", ...) here
time.sleep(3.5) 

# Standard dummy placeholder video for visual asset returning 
simulated_output_url = "https://googleapis.com"

return {
    "success": True,
    "job_status": "COMPLETED",
    "video_url": simulated_output_url,
    "credits_deducted": total_cost,
    "remaining_credits": USER_WALLET["available_credits"],
    "metadata": {
        "commercial_clearance": "VERIFIED_PASSED",
        "resolution_rendered": request.resolution,
        "consistency_token_applied": request.character_token or "NONE",
        "physics_stabilizer_index": f"v1_intensity_{request.motion_intensity}"
    }
}

if name == "main": import uvicorn uvicorn.run(app, host="127.0.0.1", port=8000)