Update app.py

app.py

@@ -1,44 +1,86 @@
 # app.py
 from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
 from huggingface_hub import hf_hub_download
 import uvicorn
+import os
+import uuid
+import shutil
 
 app = FastAPI(title="WAN2 GGUF API", version="1.0")
 
-# ✅ Download model file at startup
+# ✅ Directories
 MODEL_REPO = "calcuis/wan2-gguf"
-MODEL_FILE = "wan2.2-animate-14b-q4_0.gguf"  # pick one from repo
+MODEL_FILE = "wan2.2-animate-14b-q4_0.gguf"  # big model
+MODEL_DIR = "models"
+OUTPUT_DIR = "outputs"
+
+os.makedirs(MODEL_DIR, exist_ok=True)
+os.makedirs(OUTPUT_DIR, exist_ok=True)
 
+# ✅ Download model file at startup
 model_path = hf_hub_download(
     repo_id=MODEL_REPO,
     filename=MODEL_FILE,
-    local_dir="models"
+    local_dir=MODEL_DIR
 )
-
 print("✅ Model downloaded to:", model_path)
 
+
 # Request schema
 class PromptRequest(BaseModel):
     prompt: str
     steps: int = 20
 
+
 @app.get("/")
 def root():
     return {"message": "WAN2 GGUF API is running!"}
 
+
 @app.post("/generate")
 def generate_video(request: PromptRequest):
-    # Dummy response for now
+    """
+    Dummy video generator — for now just copies a placeholder .mp4.
+    Replace this later with actual WAN2 inference code.
+    """
+
+    # Unique filename
+    file_id = str(uuid.uuid4())
+    file_path = os.path.join(OUTPUT_DIR, f"{file_id}.mp4")
+
+    # Use a Hugging Face placeholder video
+    placeholder_url = (
+        "https://huggingface.co/datasets/huggingface/documentation-images/"
+        "resolve/main/video-placeholder.mp4"
+    )
+
+    # Download placeholder only once
+    placeholder_file = os.path.join(OUTPUT_DIR, "placeholder.mp4")
+    if not os.path.exists(placeholder_file):
+        import requests
+        r = requests.get(placeholder_url, stream=True)
+        with open(placeholder_file, "wb") as f:
+            shutil.copyfileobj(r.raw, f)
+
+    # Copy placeholder to simulate unique output
+    shutil.copy(placeholder_file, file_path)
+
     return {
         "status": "success",
         "model_file": MODEL_FILE,
-        "model_path": model_path,
         "prompt": request.prompt,
         "steps": request.steps,
+        "video_url": f"/file/{file_id}.mp4",
         "note": "Replace this with actual inference code."
     }
 
-# ✅ Ensure server stays alive in Hugging Face Spaces
+
+# ✅ Serve output videos
+app.mount("/file", StaticFiles(directory=OUTPUT_DIR), name="file")
+
+
+# ✅ Run server in HF Spaces
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=7860)