import streamlit as st
import openai
import requests
from PIL import Image
from io import BytesIO

# Access the OpenAI API key from Hugging Face Spaces secrets
openai.api_key = st.secrets["OPENAI_API_KEY"]

st.title("Intuitive Customer Avatar & Content Pillar Generator")

# User inputs for avatar generation
st.subheader("Tell Us About Your Business")
business_type = st.text_input("Business Type", placeholder="e.g., Real Estate, Fitness")
primary_service_or_product = st.text_input("Primary Service/Product", placeholder="e.g., Home sales, Personal training sessions")
unique_selling_points = st.text_input("Unique Selling Points", placeholder="What sets your business apart?")
business_location = st.text_input("Business Location", placeholder="City or region you primarily serve")

st.subheader("Your Customer Interactions")
most_common_customer_feedback = st.text_area("Common Customer Feedback", placeholder="What feedback do you often receive from customers?")
customer_challenges = st.text_input("Customer Challenges", placeholder="What challenges do your customers typically face?")
repeat_customer_characteristics = st.text_input("Repeat Customer Characteristics", placeholder="Any common traits among your repeat customers?")

if st.button('Generate Avatar and Content Pillars'):
    # Construct the prompt for text generation
    prompt_text = (
        f"Create a detailed customer avatar and suggest content pillars based on the following business details: "
        f"Business type: {business_type}, primary service/product: {primary_service_or_product}, "
        f"unique selling points: {unique_selling_points}, business location: {business_location}. "
        f"Customer feedback: {most_common_customer_feedback}, customer challenges: {customer_challenges}, "
        f"repeat customer characteristics: {repeat_customer_characteristics}."
    )

    # Call the OpenAI API for text generation
    try:
        response_text = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": prompt_text}
            ]
        )
        avatar_description = response_text.choices[0].message['content']
    except Exception as e:
        avatar_description = f"Error in generating avatar description: {e}"

    # Display the avatar description with formatted headers
    st.markdown("### Customer Avatar Description")
    st.write(avatar_description.split('\n\n')[0])  # Assuming the avatar description is the first paragraph
    st.markdown("### Content Pillars Recommendations")
    st.write('\n'.join(avatar_description.split('\n\n')[1:]))  # Assuming the rest is content pillars

    # Additional prompt for image generation
    prompt_image = f"An image representing the customer avatar based on the business type: {business_type}, and customer characteristics: {repeat_customer_characteristics}."

    # Call the OpenAI API for image generation
    try:
        response_image = openai.Image.create(
            model="dall-e-2",  # Specify DALL-E model
            prompt=prompt_image,
            n=1,
            size="1024x1024"  # Set image dimensions
        )

        # Assuming the response contains a URL to the image
        image_url = response_image['data'][0]['url']
        
        # Fetch the image from the URL
        image_response = requests.get(image_url)
        image = Image.open(BytesIO(image_response.content))

        # Display the image with a header
        st.markdown("### Generated Customer Avatar Image")
        st.image(image, caption='Generated Customer Avatar Image')

    except Exception as e:
        st.error(f"Error in generating image: {e}")