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()