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()