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import streamlit as st
import base64
import openai

# Function to encode the image to base64
def encode_image(image_file):
    return base64.b64encode(image_file.getvalue()).decode("utf-8")

# Streamlit page setup
st.set_page_config(page_title="MTSS Image Accessibility Alt Text Generator", layout="centered", initial_sidebar_state="collapsed")
st.title("MTSS Snapshot: Accessibility Image Textifier: `Alt Text`")

# Retrieve the OpenAI API key from Streamlit secrets and set it
openai.api_key = st.secrets["openai_api_key"]

# File uploader for images
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])

if uploaded_file:
    with st.expander("Image", expanded=True):
        st.image(uploaded_file, caption=uploaded_file.name, use_column_width=True)

    # Toggle for additional details input
    show_details = st.checkbox("Add details about the image")

    if show_details:
        additional_details = st.text_area("Add any additional details or context about the image here:")

    # Button to trigger the analysis
    analyze_button = st.button("Analyse the MTSS Image")

    if analyze_button:
        with st.spinner("Analyzing the image..."):
            base64_image = encode_image(uploaded_file)
            prompt_text = (
                "You are a highly knowledgeable accessibility expert. "
                "Your task is to examine the following image in detail. "
                "Provide a comprehensive, factual, and accurate explanation of what the image depicts. "
                "Highlight key elements and their significance, and present your analysis in clear, well-structured format. "
                "Create a detailed image caption in explaining in 150 words or less."
            )

            if show_details and additional_details:
                prompt_text += f"\n\nAdditional Context Provided by the User:\n{additional_details}"

            # Define the messages payload
            messages = [{
                "role": "user",
                "content": [{"type": "text", "text": prompt_text}, {"type": "image_url", "image_url": f"data:image/jpeg;base64,{base64_image}"}]
            }]

            try:
                # Make the request to OpenAI and handle streaming if required
                response = openai.chat.completions.create(model="gpt-4-vision-preview", messages=messages, max_tokens=150)
                st.write(response.choices[0].message.content)
            except Exception as e:
                st.error(f"An error occurred: {e}")
else:
    st.warning("Please upload an image.")