import gradio as gr
from transformers import pipeline

# Load AI model (FLAN-T5 for food recognition & substitution)
food_model = pipeline("text2text-generation", model="google/flan-t5-small")

# Function to get calorie data & substitute using AI
def get_food_substitute(food, portion_size):
    # AI model processes input and provides a structured response
    query = f"Suggest a lower-calorie substitute for {food}. Format: Original Food - Calories, Substitute - Calories"
    response = food_model(query, max_length=100, truncation=True)[0]['generated_text']

    # Try to extract structured values from AI response
    try:
        parts = response.split(", ")
        if len(parts) < 4:
            return "AI model returned incomplete data. Try another food item."

        original_food, original_calories, substitute, substitute_calories = parts
        original_calories = float(original_calories.split(" ")[0]) * portion_size / 100
        substitute_calories = float(substitute_calories.split(" ")[0]) * portion_size / 100
        calories_saved = original_calories - substitute_calories

        return (
            f"**Original Food:** {original_food} ({original_calories:.2f} kcal)\n"
            f"**Suggested Substitute:** {substitute} ({substitute_calories:.2f} kcal)\n"
            f"**Calories Saved:** {calories_saved:.2f} kcal"
        )
    except Exception as e:
        return f"Error: AI model output format incorrect. Details: {str(e)}"

# Gradio UI
with gr.Blocks() as app:
    gr.Markdown("# 🍽️ DeepCalorie - AI-Powered Food Substitute Finder")
    gr.Markdown("Enter a food item, and get a **lower-calorie alternative** along with calories saved.")

    with gr.Row():
        food_input = gr.Textbox(label="Enter Food/Dish")
        portion_input = gr.Number(label="Portion Size (grams)", value=100)

    submit_button = gr.Button("Find Substitute")
    output = gr.Markdown()

    submit_button.click(get_food_substitute, inputs=[food_input, portion_input], outputs=output)

# Run the app
if __name__ == "__main__":
    app.launch()