RG_PPLX_FixZGPU_CudaInitinsideGPUFunc

app.py

@@ -1,14 +1,12 @@
 
 import gradio as gr
-import spaces  # Import spaces for ZeroGPU decorator
+import spaces  # For ZeroGPU decorator
 
 from transformers import pipeline
 from diffusers import StableDiffusionPipeline
 import torch
 import requests
 import re
-import os
-import json
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 from tempfile import NamedTemporaryFile
@@ -36,15 +34,12 @@ def get_calories(ingredient):
     nutrients = get_nutrients(ingredient)
     return nutrients.get("Energy", 0.0)
 
-# --- Models ---
+# --- Load CPU-only model globally (safe) ---
 recipe_model = pipeline("text-generation", model="samdak93/qrit-2")
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-image_model = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
-    torch_dtype=torch.float16 if device == "cuda" else torch.float32
-).to(device)
+# Globals to cache GPU models after loading
+image_model = None
+recipe_model_gpu = None  # If you want to move recipe_model to GPU, handle similarly
 
 # --- Utilities ---
 forbidden = ["pork", "bacon", "ham", "lard", "gelatin", "alcohol", "beer", "wine", "rum", "whiskey", "vodka", "gin"]
@@ -75,10 +70,22 @@ def get_nutrient_breakdown(ingredients):
         breakdown[ing] = get_nutrients(ing)
     return breakdown
 
-@spaces.GPU(duration=120)  # Decorate for ZeroGPU with 120s duration
+@spaces.GPU(duration=120)
+def load_image_model():
+    global image_model
+    if image_model is None:
+        image_model = StableDiffusionPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5",
+            torch_dtype=torch.float16
+        ).to("cuda")
+    return image_model
+
+@spaces.GPU(duration=120)
 def generate_image(prompt):
-    with torch.autocast(device):
-        return image_model(prompt).images[0]
+    pipe = load_image_model()
+    with torch.autocast("cuda"):
+        image = pipe(prompt).images[0]
+    return image
 
 def export_pdf(recipe_text, nutrients):
     temp_pdf = NamedTemporaryFile(delete=False, suffix=".pdf")
@@ -109,7 +116,6 @@ def export_pdf(recipe_text, nutrients):
     c.save()
     return temp_pdf.name
 
-@spaces.GPU  # Decorate audio generation if needed; can be CPU but GPU decorator is safe
 def generate_audio(recipe_text):
     tts = gTTS(text=recipe_text)
     temp_audio = NamedTemporaryFile(delete=False, suffix=".mp3")