import gradio as gr
from huggingface_hub import InferenceClient

theme = gr.themes.Ocean(
    primary_hue="red",
    secondary_hue="emerald",
    neutral_hue="amber",
)

client = InferenceClient("microsoft/phi-4")

def respond(message, history, budget, have_items, allergies):
    # Start with a system prompt
    instruction = "You are a friendly chatbot that is designed to help users find recipes they can cook with ingredients they already have at home. "
    instruction += "The current user has " + str(have_items) + "at home. "
    instruction += "The current user is allergic to: " + str(allergies) + ". These ingredients must under no circumstance feature in the recipes you provide. "
    instruction += "For purchasing any additional items, the current user has a budget of: " + str(budget) + "£. "
    instruction += "After the user says what they want to have, you must respond with a recipe you find online, please lay out your response in the following way: "
    instruction += """
        NAME OF THE RECIPE AS A TITLE WITH A BORDER USING = SIGNS
        the ingredients required for the recipe listed (in brackets the cost of any ingredients the user doesn't already have)
        a short (max 350 words) summary of the method for making the meal detailing any special appliances required
        a link to the source from which you found the recipe
        a message asking the user if they want to make this recipe, want more info on where to buy ingredients, or if they want something else.
        (depending on the user's response you must respond to their request, if it is for another recipe, follow the exact same format)
    """
    messages = [{"role": "system", "content": instruction}]
    # Convert Gradio history (list of [user, assistant] pairs) to OpenAI-style message format
    for user_msg, bot_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if bot_msg:
            messages.append({"role": "assistant", "content": bot_msg})
    # Append the new user message
    messages.append({"role": "user", "content": message})
    # Call the model
    response = client.chat_completion(
        messages=messages,
        max_tokens=500
    )
    return response["choices"][0]["message"]["content"].strip()

with gr.Blocks(theme=theme) as demo:
    gr.Markdown("## RecipEASY 🍳")
    budget = gr.Slider(
        minimum=0,     # lowest value
        maximum=100,   # highest value
        step=1,        # increment step
        label="Enter Budget:"
    )
    have_items = gr.Textbox(label="Ingredients you have", placeholder="e.g. pasta, tomato, garlic")
    allergies = gr.Textbox(label="Dietary restrictions", placeholder="e.g. gluten, soy")
    gr.ChatInterface(
        fn=respond,
        additional_inputs=[budget, have_items, allergies]
    )

demo.launch()