import streamlit as st
from PIL import Image
from image_classifier import classify_food_with_pipeline
from recipe_fetcher import fetch_recipe
from pdf_generator import generate_pdf

# Function to display recipes in a readable format
def display_recipes(recipes):
    recipe_text = ""
    if recipes:
        for recipe in recipes:
            recipe_text += f"**Title**: {recipe['title']}\n"
            recipe_text += "**Ingredients**:\n"
            for ingredient in recipe['ingredients'].split('|'):
                recipe_text += f"- {ingredient}\n"
            recipe_text += f"**Servings**: {recipe['servings']}\n"
            recipe_text += "**Instructions**:\n"
            recipe_text += f"{recipe['instructions'][:300]}...\n"  # Truncate for brevity
            recipe_text += "-" * 40 + "\n"
    else:
        recipe_text = "No recipes found."
    
    return recipe_text

# Main Streamlit app UI
def main():
    st.title("Food Classifier and Recipe Finder")
    st.write("Choose an option to get food recipes.")

    # Radio buttons to select the mode (search or upload)
    option = st.radio("Choose an option", ("Search Food Recipe", "Upload Image to Predict Food"))

    # If 'Search Food Recipe' is selected
    if option == "Search Food Recipe":
        query = st.text_input("Enter a food name", "")
        if query:
            recipes = fetch_recipe(query)
            recipe_text = display_recipes(recipes)
            st.text_area("Recipe Details", recipe_text, height=300)

            # Only show PDF download button if recipes are found
            if "No recipes found." not in recipe_text:
                pdf_file = generate_pdf(recipe_text, query)
                with open(pdf_file, "rb") as f:
                    st.download_button("Download Recipe as PDF", f, file_name=pdf_file)

    # If 'Upload Image to Predict Food' is selected
    elif option == "Upload Image to Predict Food":
        st.write("Upload an image to predict the food item and get the recipe.")

        image_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

        if image_file is not None:
            # Open the uploaded image
            image = Image.open(image_file).convert("RGB")
            
            # Display the image
            st.image(image, caption="Uploaded Image", use_container_width=True)

            # Classify the food using the pipeline
            label_with_pipeline = classify_food_with_pipeline(image)
            st.write(f"**Predicted Food**: {label_with_pipeline}")

            # Fetch the recipe based on the predicted label
            recipes = fetch_recipe(label_with_pipeline)
            
            # Display the fetched recipe(s)
            recipe_text = display_recipes(recipes)
            st.text_area("Recipe Details", recipe_text, height=300)

            # Only show PDF download button if recipes are found
            if "No recipes found." not in recipe_text:
                pdf_file = generate_pdf(recipe_text, label_with_pipeline)
                with open(pdf_file, "rb") as f:
                    st.download_button("Download Recipe as PDF", f, file_name=pdf_file)
# Add monogram at the bottom of the Streamlit app
    st.markdown("<br><br><h5 style='text-align: center;'>Developed by M.Nabeel</h5>", unsafe_allow_html=True)

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