RG2.0

app.py

@@ -1,21 +1,22 @@
-# -*- coding: utf-8 -*-
-# app.py
-
 import gradio as gr
-from transformers import pipeline
-from diffusers import StableDiffusionPipeline
-import torch
+from huggingface_hub import InferenceClient
 import requests
 import re
 import os
 import json
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
-from tempfile import NamedTemporaryFile
 from gtts import gTTS
-import spaces  # For ZeroGPU preload
+from tempfile import NamedTemporaryFile
 
-# === USDA API Setup ===
+# --- Load HF Token from environment ---
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# --- Inference Clients ---
+text_client = InferenceClient("samdak93/qrit-2", token=HF_TOKEN)
+image_client = InferenceClient("runwayml/stable-diffusion-v1-5", token=HF_TOKEN)
+
+# --- USDA API Setup ---
 USDA_API_KEY = "gcwe1gXmMveg7buqddggl6wAZa7Sd7wrZV87P31z"
 USDA_SEARCH_URL = "https://api.nal.usda.gov/fdc/v1/foods/search"
 
@@ -37,27 +38,7 @@ def get_calories(ingredient):
     nutrients = get_nutrients(ingredient)
     return nutrients.get("Energy", 0.0)
 
-# === 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 ===
+# --- Utilities ---
 forbidden = ["pork", "bacon", "ham", "lard", "gelatin", "alcohol", "beer", "wine", "rum", "whiskey", "vodka", "gin"]
 
 def parse_ingredients(text):
@@ -70,7 +51,7 @@ 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 = recipe_model(prompt, max_length=300, num_return_sequences=1)[0]['generated_text']
+        out = text_client.text_generation(prompt, max_new_tokens=300)
         if any(h in out.lower() for h in forbidden):
             continue
 
@@ -87,8 +68,8 @@ def get_nutrient_breakdown(ingredients):
     return breakdown
 
 def generate_image(prompt):
-    with torch.autocast(device):
-        return image_model(prompt).images[0]
+    image = image_client.text_to_image(prompt)
+    return image
 
 def export_pdf(recipe_text, nutrients):
     temp_pdf = NamedTemporaryFile(delete=False, suffix=".pdf")
@@ -125,7 +106,6 @@ 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:
@@ -141,9 +121,9 @@ 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 Interface ===
+# --- Gradio UI ---
 with gr.Blocks() as demo:
-    gr.Markdown("## 🕌 Halal Recipe Generator - Advanced Edition")
+    gr.Markdown("## 🍏 Halal Recipe Generator - Hugging Face Spaces Edition")
     with gr.Row():
         key_input = gr.Textbox(label="Key Ingredient")
         submit_btn = gr.Button("Generate Recipe")
@@ -157,5 +137,6 @@ with gr.Blocks() as demo:
     submit_btn.click(app, inputs=key_input,
                      outputs=[output_text, output_img, output_table, output_pdf, output_audio])
 
-# Launch app
-demo.launch(share=True)
+if __name__ == '__main__':
+    demo.launch()
+

requirements.txt

@@ -1,10 +1,6 @@
-gradio>=4.0
-transformers>=4.35.0
-diffusers>=0.24.0
-accelerate>=0.23.0
-torch>=2.1.0
+gradio
+huggingface_hub
+requests
 reportlab
 gTTS
-requests
-spaces