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e8ed882 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | from huggingface_hub import InferenceClient
from PIL import Image
import gradio as gr
import os
import json
# Function to generate recipe from ingredients
def generate_recipe(ingredients: str, model_name_recipe: str, token: str) -> str:
"""
Generates a recipe based on input ingredients using an LLM.
Args:
ingredients (str): Ingredients input by the user.
model_name_recipe (str): Hugging Face model for text generation.
token (str): API token for Hugging Face authentication.
Returns:
str: Generated recipe text.
"""
prompt = f"Generate a recipe using the following ingredients: {ingredients}. Write the recipe in detail along with a suitable title."
client = InferenceClient(model_name_recipe, token=token)
try:
response = client.post(
json={
"inputs": prompt,
"parameters": {"max_new_tokens": 500},
"task": "text-generation",
}
)
return json.loads(response.decode())[0]["generated_text"]
except Exception as e:
return f"Error generating recipe: {e}"
# Function to generate image of the recipe
def generate_image(recipe_title: str, model_name_image: str, token: str) -> Image.Image:
"""
Generates an image based on the recipe title using a text-to-image model.
Args:
recipe_title (str): Title of the recipe to use as the prompt.
model_name_image (str): Hugging Face model for image generation.
token (str): API token for Hugging Face authentication.
Returns:
PIL.Image.Image: Generated image.
"""
client = InferenceClient(model_name_image, token=token)
try:
prompt = f"A photo the dish: {recipe_title}, showing delicious presentation."
return client.text_to_image(prompt)
except Exception as e:
print(f"Error generating image: {e}")
return None
# Gradio function that combines both recipe generation and image generation
def generate_recipe_and_image(message: str, history):
token = os.getenv("API_Token_HF_AIrecipe") # Ensure your Hugging Face token is set in environment variables
model_name_recipe = "microsoft/Phi-3-mini-4k-instruct" # Replace with the LLM model of your choice
model_name_image = "prompthero/openjourney" # Replace with the image generation model of your choice
# Generate recipe
recipe_text = generate_recipe(message, model_name_recipe, token)
prompt_to_remove = f"Generate a recipe using the following ingredients: {message}. Write the recipe in detail along with a suitable title."
if prompt_to_remove in recipe_text:
recipe_text = recipe_text.replace(prompt_to_remove, "").strip()
# print("recipe_text:",recipe_text)
# Extract recipe title from generated recipe text
lines = recipe_text.split("\n")
for line in lines:
if line.lower().startswith("title:"): # Case-insensitive match
recipe_title = line.replace("Title:", "").strip() # Extract and clean title
break
print("recipe_title",recipe_title)
# Generate image for the recipe
recipe_image = generate_image(recipe_title, model_name_image, token)
image_path = "./recipe_image.png"
recipe_image.save(image_path, format="PNG")
# Combine text and image
return recipe_text, gr.Image(value=image_path)
if __name__ == "__main__":
# Gradio UI
with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
gr_image = gr.Image(render=False)
with gr.Row():
with gr.Column(scale=4):
gr.Markdown("<center><h1>AI Recipe Chatbot</h1></center>")
chatbot = gr.ChatInterface(
generate_recipe_and_image,
examples=["chicken, rice, tomatoes, onions, spices", "oats, milk, fruits", "Noodles, Tofu, Soy sauce"],
type="messages",
additional_outputs=[gr_image]
)
with gr.Column(scale=1):
gr.Markdown("<center><h1>Recipe Image</h1></center>")
# recipe_image = gr.Image(label="Generated Recipe Image")
gr_image.render()
demo.launch()
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