Add application and requirements files

app.py

@@ -1,6 +1,6 @@
+# -*- coding: utf-8 -*-
 # app.py
 
-from spaces import GPU
 import gradio as gr
 from transformers import pipeline
 from diffusers import StableDiffusionPipeline
@@ -13,21 +13,9 @@ from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 from tempfile import NamedTemporaryFile
 from gtts import gTTS
+import spaces  # For ZeroGPU preload
 
-# --- GPU preload hook ---
-@GPU
-def preload():
-    global recipe_model, image_model
-
-    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)
-
-
-# --- USDA API Setup ---
+# === USDA API Setup ===
 USDA_API_KEY = "gcwe1gXmMveg7buqddggl6wAZa7Sd7wrZV87P31z"
 USDA_SEARCH_URL = "https://api.nal.usda.gov/fdc/v1/foods/search"
 
@@ -49,14 +37,34 @@ def get_calories(ingredient):
     nutrients = get_nutrients(ingredient)
     return nutrients.get("Energy", 0.0)
 
-# --- Utilities ---
+# === Globals for Models ===
+recipe_model = None
+image_model = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# === Preload Models ===
+@spaces.GPU
+def preload():
+    global recipe_model, image_model
+    print(f"🚀 Preloading models on {device}...")
+
+    recipe_model = pipeline("text-generation", model="samdak93/qrit-2", device=0 if device == "cuda" else -1)
+
+    image_model = StableDiffusionPipeline.from_pretrained(
+        "runwayml/stable-diffusion-v1-5",
+        torch_dtype=torch.float16 if device == "cuda" else torch.float32
+    ).to(device)
+
+    print("✅ Models preloaded successfully.")
+
+# === Recipe Generation Utilities ===
 forbidden = ["pork", "bacon", "ham", "lard", "gelatin", "alcohol", "beer", "wine", "rum", "whiskey", "vodka", "gin"]
 
 def parse_ingredients(text):
     match = re.search(r"(?i)ingredients:(.*?)(\n|directions:|instructions:|$)", text, re.DOTALL)
     if match:
         ingredients_block = match.group(1).strip()
-        return list(set([line.strip("- *") for line in ingredients_block.split("\n") if line.strip()]))
+        return [line.strip("- *") for line in ingredients_block.split("\n") if line.strip()]
     return []
 
 def generate_recipe(key_ingredient):
@@ -79,7 +87,6 @@ def get_nutrient_breakdown(ingredients):
     return breakdown
 
 def generate_image(prompt):
-    device = "cuda" if torch.cuda.is_available() else "cpu"
     with torch.autocast(device):
         return image_model(prompt).images[0]
 
@@ -118,6 +125,7 @@ def generate_audio(recipe_text):
     tts.save(temp_audio.name)
     return temp_audio.name
 
+# === Core App Function ===
 def app(key_ingredient):
     recipe_text, ingredients, calories = generate_recipe(key_ingredient)
     if not ingredients:
@@ -133,9 +141,9 @@ def app(key_ingredient):
     display_text = f"{recipe_text}\n\nEstimated Calories: {int(calories)} kcal"
     return display_text, image, nutrients, pdf_path, audio_path
 
-# --- Gradio UI ---
+# === Gradio Interface ===
 with gr.Blocks() as demo:
-    gr.Markdown("## 🕌 Halal Recipe Generator - Advanced Edition (GPU-Enabled)")
+    gr.Markdown("## 🕌 Halal Recipe Generator - Advanced Edition")
     with gr.Row():
         key_input = gr.Textbox(label="Key Ingredient")
         submit_btn = gr.Button("Generate Recipe")
@@ -149,4 +157,5 @@ with gr.Blocks() as demo:
     submit_btn.click(app, inputs=key_input,
                      outputs=[output_text, output_img, output_table, output_pdf, output_audio])
 
-demo.launch()
+# Launch app
+demo.launch(share=True)

requirements.txt

@@ -1,8 +1,10 @@
-gradio
-transformers
-diffusers
-accelerate
-torch
+gradio>=4.0
+transformers>=4.35.0
+diffusers>=0.24.0
+accelerate>=0.23.0
+torch>=2.1.0
 reportlab
 gTTS
 requests
+spaces
+