RG_PPLX_LdMd

app.py

@@ -1,22 +1,18 @@
+
 import gradio as gr
-from huggingface_hub import InferenceClient
-import spaces  # For @spaces.GPU decorator
+import spaces  # 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 gtts import gTTS
 from tempfile import NamedTemporaryFile
-
-# --- Load HF Token from environment ---
-HF_TOKEN = os.getenv("HF_FINEGRAIN_TOKEN")
-
-# --- Inference Clients ---
-text_client = InferenceClient("samdak93/qrit-2", token=HF_TOKEN)
-image_client = InferenceClient("runwayml/stable-diffusion-v1-5", token=HF_TOKEN)
+from gtts import gTTS
 
 # --- USDA API Setup ---
 USDA_API_KEY = "gcwe1gXmMveg7buqddggl6wAZa7Sd7wrZV87P31z"
@@ -40,6 +36,16 @@ def get_calories(ingredient):
     nutrients = get_nutrients(ingredient)
     return nutrients.get("Energy", 0.0)
 
+# --- Models ---
+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)
+
 # --- Utilities ---
 forbidden = ["pork", "bacon", "ham", "lard", "gelatin", "alcohol", "beer", "wine", "rum", "whiskey", "vodka", "gin"]
 
@@ -53,7 +59,7 @@ def parse_ingredients(text):
 def generate_recipe(key_ingredient):
     for _ in range(5):
         prompt = f"Create a halal recipe under 2000 calories that includes {key_ingredient}.\nIngredients:"
-        out = text_client.text_generation(prompt, max_new_tokens=300)
+        out = recipe_model(prompt, max_length=300, num_return_sequences=1)[0]['generated_text']
         if any(h in out.lower() for h in forbidden):
             continue
 
@@ -69,10 +75,10 @@ def get_nutrient_breakdown(ingredients):
         breakdown[ing] = get_nutrients(ing)
     return breakdown
 
-@spaces.GPU
+@spaces.GPU(duration=120)  # Decorate for ZeroGPU with 120s duration
 def generate_image(prompt):
-    image = image_client.text_to_image(prompt)
-    return image
+    with torch.autocast(device):
+        return image_model(prompt).images[0]
 
 def export_pdf(recipe_text, nutrients):
     temp_pdf = NamedTemporaryFile(delete=False, suffix=".pdf")
@@ -103,6 +109,7 @@ 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")
@@ -116,6 +123,7 @@ def app(key_ingredient):
 
     title_match = re.search(r"(?i)^title: (.+)$", recipe_text, re.MULTILINE)
     title = title_match.group(1).strip() if title_match else f"Dish with {key_ingredient}"
+
     image = generate_image(f"A delicious {title}, beautifully plated")
     nutrients = get_nutrient_breakdown(ingredients)
     pdf_path = export_pdf(recipe_text, nutrients)
@@ -126,7 +134,7 @@ def app(key_ingredient):
 
 # --- Gradio UI ---
 with gr.Blocks() as demo:
-    gr.Markdown("## 🍏 Halal Recipe Generator - Hugging Face Spaces Edition")
+    gr.Markdown("## 🕌 Halal Recipe Generator - Advanced Edition")
     with gr.Row():
         key_input = gr.Textbox(label="Key Ingredient")
         submit_btn = gr.Button("Generate Recipe")
@@ -140,6 +148,5 @@ with gr.Blocks() as demo:
     submit_btn.click(app, inputs=key_input,
                      outputs=[output_text, output_img, output_table, output_pdf, output_audio])
 
-if __name__ == '__main__':
-    demo.launch()
+demo.launch(share=True, pwa=True, debug=True)
 

requirements.txt

@@ -1,6 +1,10 @@
+
 gradio
-huggingface_hub
-requests
+transformers
+diffusers
+accelerate
+torch
 reportlab
 gTTS
+requests