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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 HuggingFace inference endpoint
llm = HuggingFaceEndpoint(
repo_id="mistralai/Mistral-7B-Instruct-v0.3",
huggingfacehub_api_token=HF_TOKEN.strip(),
temperature=0.7,
)
# Input validation function
def validate_ingredients(ingredients):
prompt = (
f"Review the provided list of items: {ingredients}. "
f"Determine if all items are valid food ingredients. "
f"Respond only with 'Valid' if all are valid food items or 'Invalid' if any are not."
)
response = llm(prompt)
return response.strip()
# Recipe generation function
def generate_recipe(ingredients):
prompt = (
f"You are an expert chef. Using the ingredients: {ingredients}, "
f"suggest two recipes. Provide a title, preparation time, and step-by-step instructions. "
f"It is not mandatory to include all ingredients, pick the ingredients required for each recipe. "
f"Do not include the ingredient list explicitly in the response."
)
response = llm(prompt)
return response.strip()
# Combined function for Gradio
def suggest_recipes(ingredients):
validation_result = validate_ingredients(ingredients)
if validation_result == "Valid":
return generate_recipe(ingredients)
else:
return "I'm sorry, but I can't process this request due to invalid ingredients. Please provide valid ingredients for cooking!"
# Gradio interface
with gr.Blocks() as app:
gr.Markdown("# Recipe Suggestion App")
gr.Markdown("Provide the ingredients you have, and this app will validate them and suggest a recipe!")
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 or Validation Result:", 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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