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import os
from dotenv import load_dotenv
import gradio as gr
from langchain_huggingface import HuggingFaceEndpoint
# Load environment variables
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")
# Initialize the Hugging Face endpoint
llm = HuggingFaceEndpoint(
repo_id="mistralai/Mistral-7B-Instruct-v0.3", # Replace with the desired Hugging Face model
huggingfacehub_api_token=HF_TOKEN.strip(),
temperature=0.7,
max_new_tokens=300
)
# Recipe generation function
def suggest_recipes(ingredients):
prompt = (
f"You are an expert chef. Please suggest 3 unique recipes using the following "
f"ingredients: {ingredients}. Provide a title for each recipe, include "
f"preparation time, and list step-by-step directions."
)
try:
response = llm(prompt)
# Format response into multiple recipes
generated_text = response.content
recipes = generated_text.split("Recipe")
structured_recipes = []
for i, recipe in enumerate(recipes):
if recipe.strip(): # Ensure non-empty recipe
structured_recipes.append(f"Recipe {i+1}:\n{recipe.strip()}")
return "\n\n".join(structured_recipes)
except Exception as e:
return f"Error: {e}"
# Gradio interface
with gr.Blocks() as app:
gr.Markdown("# AI Recipe Generator")
gr.Markdown("Enter the ingredients you have, and this app will generate 3 unique recipes along with preparation times!")
with gr.Row():
ingredients_input = gr.Textbox(
label="Enter Ingredients (comma-separated):",
placeholder="e.g., eggs, milk, flour"
)
recipe_output = gr.Textbox(label="Suggested Recipes:", lines=15, interactive=False)
generate_button = gr.Button("Get Recipes")
generate_button.click(suggest_recipes, inputs=ingredients_input, outputs=recipe_output)
# Launch the app
app.launch()
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