import streamlit as st
from transformers import pipeline
from PIL import Image
from huggingface_hub import InferenceClient
import os
import openai  # Added import

# Set page configuration
st.set_page_config(
    page_title="DelishAI - Your Culinary Assistant",
    page_icon="🍽️",
    layout="centered",
    initial_sidebar_state="expanded",
)

def local_css():
    st.markdown(
        """
        <style>
        /* [Your existing CSS styles here] */
        </style>
        """, unsafe_allow_html=True
    )

local_css()  # Apply the CSS

# Hugging Face API key
API_KEY = st.secrets["HF_API_KEY"]

# Initialize the Hugging Face Inference Client
client = InferenceClient(api_key=API_KEY)

# Load the image classification pipeline
@st.cache_resource
def load_image_classification_pipeline():
    """ Load the image classification pipeline using a pretrained model. """
    return pipeline("image-classification", model="Shresthadev403/food-image-classification")

pipe_classification = load_image_classification_pipeline()

# Function to generate ingredients using Hugging Face Inference Client
def get_ingredients_qwen(food_name):
    """ Generate a list of ingredients for the given food item using Qwen NLP model. Returns a clean, comma-separated list of ingredients. """
    messages = [
        {
            "role": "user",
            "content": f"List only the main ingredients for {food_name}. "
                       f"Respond in a concise, comma-separated list without any extra text or explanations."
        }
    ]
    try:
        completion = client.chat.completions.create(
            model="Qwen/Qwen2.5-Coder-32B-Instruct", messages=messages, max_tokens=50
        )
        generated_text = completion.choices[0].message["content"].strip()
        return generated_text
    except Exception as e:
        return f"Error generating ingredients: {e}"

# **Set OpenAI API Key**
openai.api_key = st.secrets["openai"]["api_key"]  # Ensure you have this in your secrets

# Main content
st.markdown('<div class="title"><h1>DelishAI - Your Culinary Assistant</h1></div>', unsafe_allow_html=True)

# Add banner image
st.image("IR_IMAGE.png", use_container_width=True)

# Sidebar for model information (hidden on small screens)
with st.sidebar:
    st.title("Model Information")
    st.write("**Image Classification Model**")
    st.write("Shresthadev403/food-image-classification")
    st.write("**LLM for Ingredients**")
    st.write("Qwen/Qwen2.5-Coder-32B-Instruct")
    st.markdown("---")
    st.markdown("<p style='text-align: center;'>Developed by Muhammad Hassan Butt.</p>", unsafe_allow_html=True)

# Assuming `sample_images` is defined somewhere in your code
sample_images = {
    "Pizza": "path_to_pizza_image.jpg",
    "Salad": "path_to_salad_image.jpg",
    # Add more sample images as needed
}

cols = st.columns(len(sample_images))
for idx, (name, file_path) in enumerate(sample_images.items()):
    with cols[idx]:
        if st.button(f"{name}", key=name):
            uploaded_file = file_path

# File uploader
st.subheader("Upload a food image:")
uploaded_file = st.file_uploader("", type=["jpg", "png", "jpeg"])
if 'uploaded_file' in locals() and uploaded_file is not None:
    # Display the uploaded image
    if isinstance(uploaded_file, str):  # Sample image selected
        image = Image.open(uploaded_file)
    else:  # User uploaded image
        image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_container_width=True)

    # Classification button
    if st.button("Classify"):
        with st.spinner("Classifying..."):
            # Make predictions
            predictions = pipe_classification(image)
            # Display only the top prediction
            top_food = predictions[0]['label']
            st.header(f"🍽️ Food: {top_food}")
           
            # Generate and display ingredients for the top prediction
            st.subheader("📝 Ingredients")
            try:
                ingredients = get_ingredients_qwen(top_food)
                st.write(ingredients)
            except Exception as e:
                st.error(f"Error generating ingredients: {e}")

            # **Healthier Alternatives using OpenAI API**
            st.subheader("💡 Healthier Alternatives")
            try:
                response = openai.ChatCompletion.create(
                    model="gpt-4o",  # You can choose the model you prefer
                    messages=[
                        {
                            "role": "system",
                            "content": "You are a helpful assistant specializing in providing healthy alternatives to various dishes."
                        },
                        {
                            "role": "user",
                            "content": f"What's a healthy {top_food} recipe, and why is it healthy?"
                        }
                    ],
                    max_tokens=200,  # Adjust as needed
                    temperature=0.7,  # Adjust creativity level as needed
                )
                result = response['choices'][0]['message']['content'].strip()
                st.write(result)
            except Exception as e:
                st.error(f"Unable to generate healthier alternatives: {e}")
else:
    st.info("Please select or upload an image to get started.")