Update app.py

app.py

@@ -5,9 +5,10 @@ from pydantic import BaseModel
 import uvicorn
 import os
 import uuid
+import subprocess
+import json
 
-# GGUF & video libraries
-from gguf_runtime import GGUFModel  # Make sure gguf-runtime is installed
+from huggingface_hub import hf_hub_download
 from moviepy.editor import ImageSequenceClip
 
 app = FastAPI(title="WAN2 GGUF API", version="1.0")
@@ -17,13 +18,12 @@ MODEL_REPO = "calcuis/wan2-gguf"
 MODEL_FILE = "wan2.2-animate-14b-q4_0.gguf"
 MODEL_DIR = "models"
 OUTPUT_DIR = "outputs"
+NODE_CLI = "gguf-node-cli.js"  # Path to your gguf-node CLI
 
 os.makedirs(MODEL_DIR, exist_ok=True)
 os.makedirs(OUTPUT_DIR, exist_ok=True)
 
 # -------------------- Download model --------------------
-from huggingface_hub import hf_hub_download
-
 model_path = hf_hub_download(
     repo_id=MODEL_REPO,
     filename=MODEL_FILE,
@@ -31,16 +31,30 @@ model_path = hf_hub_download(
 )
 print("✅ Model downloaded to:", model_path)
 
-# -------------------- Load GGUF model --------------------
-print("Loading WAN2 GGUF model...")
-wan2_model = GGUFModel(model_path)
-print("✅ WAN2 model loaded")
-
 # -------------------- Request schema --------------------
 class PromptRequest(BaseModel):
     prompt: str
     steps: int = 20
 
+# -------------------- Helper: GGUF Node CLI --------------------
+def generate_frames_with_node(prompt, steps=20):
+    """
+    Calls gguf-node CLI to generate frames.
+    CLI should output JSON array of frame image paths.
+    """
+    try:
+        result = subprocess.run(
+            ["node", NODE_CLI, "--model", model_path, "--prompt", prompt, "--steps", str(steps)],
+            capture_output=True,
+            text=True,
+            check=True
+        )
+        frames = json.loads(result.stdout)
+        return frames
+    except subprocess.CalledProcessError as e:
+        print("Error calling gguf-node:", e.stderr)
+        return []
+
 # -------------------- Routes --------------------
 @app.get("/")
 def root():
@@ -58,12 +72,11 @@ def generate_video(request: PromptRequest):
     file_id = str(uuid.uuid4())
     file_path = os.path.join(OUTPUT_DIR, f"{file_id}.mp4")
 
-    # ---- WAN2 inference ----
-    # This generates frames from prompt
-    frames = wan2_model.generate_video(
-        prompt=request.prompt,
-        steps=request.steps
-    )
+    # ---- WAN2 inference via Node CLI ----
+    frames = generate_frames_with_node(request.prompt, request.steps)
+
+    if not frames:
+        return {"status": "error", "message": "Failed to generate frames"}
 
     # ---- Save frames as MP4 ----
     clip = ImageSequenceClip(frames, fps=12)