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app.py

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+from transformers import pipeline
+import requests
+import io
+from PIL import Image
+import gradio as gr
+import tempfile
+import os
+
+HF = os.getenv("HF_SECRET")
+recipe_generator = pipeline("text2text-generation", model="flax-community/t5-recipe-generation")
+
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
+headers = {'Authorization': f'Bearer {HF}'}
+
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.content
+
+def generate(input):
+    inputs = [f"items: {item}" for item in [input]]
+    generated_recipes = recipe_generator(inputs, max_length=512, min_length=64, no_repeat_ngram_size=3, do_sample=True, top_k=60, top_p=0.95)
+    recipe_text = generated_recipes[0]['generated_text']
+  
+    #make recipe better into json?
+    image_bytes = query({
+    	"inputs": str(recipe_text),
+    })
+    image = Image.open(io.BytesIO(image_bytes))
+
+    temp_file = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
+    image.save(temp_file, format="JPEG")
+    temp_file.close() 
+
+    return recipe_text, temp_file.name
+
+demo = gr.Interface(
+    fn=generate,  # Function to process input
+    inputs=gr.Textbox(label="Enter ingredients separated by commas"),  # Text input on the left
+    outputs=[gr.Textbox(label="Recipe Display"), gr.Image(label="Recipe Image")]
+)  
+demo.launch()

requirements.txt

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+transformers
+numpy
+gradio
+torch
+requests
+Pillow