actualizaciones

services/cms-service/scripts/video_bridge.py

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+import os
+import time
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import torch
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+import uuid
+
+app = FastAPI()
+
+# Configuration
+MODEL_ID = "runwayml/stable-diffusion-v1-5"
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+OUTPUT_DIR = os.path.join(BASE_DIR, "outputs")
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+# Global variables for the model
+pipe = None
+
+def load_model():
+    global pipe
+    print("Loading Stable Diffusion model on CPU...")
+    pipe = StableDiffusionPipeline.from_pretrained(
+        MODEL_ID, 
+        torch_dtype=torch.float32,
+    )
+    pipe.to("cpu")
+    # pipe.enable_model_cpu_offload() 
+    pipe.enable_attention_slicing()
+    print("Model loaded successfully.")
+
+from contextlib import asynccontextmanager
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Model loading is heavy, we'll do it on first request to avoid timeout at startup
+    yield
+
+app = FastAPI(lifespan=lifespan)
+
+# Serve generated images
+from fastapi.staticfiles import StaticFiles
+app.mount("/outputs", StaticFiles(directory=OUTPUT_DIR), name="outputs")
+
+class ImageRequest(BaseModel):
+    prompt: str
+    lesson_id: str
+
+@app.post("/generate")
+async def generate_image(request: ImageRequest):
+    global pipe
+    if pipe is None:
+        load_model()
+    
+    try:
+        print(f"Generating image for prompt: {request.prompt}")
+        
+        generator = torch.manual_seed(42)
+        # Using a small number of steps for speed since it's on CPU
+        image = pipe(
+            request.prompt,
+            num_inference_steps=20,
+            generator=generator
+        ).images[0]
+        
+        image_filename = f"image_{request.lesson_id}_{uuid.uuid4().hex[:8]}.png"
+        image_path = os.path.join(OUTPUT_DIR, image_filename)
+        
+        image.save(image_path)
+        
+        # Return the absolute URL pointing to t-800 so the frontend can find it
+        hostname = os.getenv("BRIDGE_HOSTNAME", "localhost")
+        full_url = f"http://{hostname}:8080/outputs/{image_filename}"
+        
+        return {"status": "completed", "url": full_url}
+        
+    except Exception as e:
+        print(f"Generation error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/health")
+async def health():
+    return {"status": "ok", "model_loaded": pipe is not None}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8080)