app.py

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")

#Add the image with a specified width
image_width = 300  # Set the desired width in pixels
st.image('MTSS.ai_Logo.png', width=image_width)

st.title('VisionText™ | Accessibility')
st.subheader(':green[_Image Alt Text Generator_]')

# Retrieve the OpenAI API Key from secrets
openai.api_key = st.secrets["openai_api_key"]

# File uploader allows user to add their own image
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])

if uploaded_file:
    # Display the uploaded image with specified width
    image_width = 150  # Set the desired width in pixels
    with st.expander("Image", expanded=True):
        st.image(uploaded_file, caption=uploaded_file.name, width=image_width, use_column_width=False)

# Toggle for showing additional details input
show_details = st.toggle("Add details about the image. ", value=False)

# Toggle for modifying the prompt for complex images
complex_image = st.toggle("Is this a complex image? ", value=False)

if show_details:
    # Text input for additional details about the image, shown only if toggle is True
    additional_details = st.text_area(
        "The details could include specific information that is important to include in the alt text or reflect why the image is being used:",
        disabled=not show_details
    )

# Button to trigger the analysis
analyze_button = st.button("Analyze the Image", type="secondary")

# Optimized prompt for complex images
complex_image_prompt_text = (
    "As an expert in image accessibility and alternative text, thoroughly describe the image provided. "
    "Provide a brief description using not more than 500 characters that convey the essential information conveyed by the image in eight or fewer clear and concise sentences. "
    "Skip phrases like 'image of' or 'picture of.' "
    "Your description should form a clear, well-structured, and factual paragraph that avoids bullet points, focusing on creating a seamless narrative."
)

# Check if an image has been uploaded, if the API key is available, and if the button has been pressed
if uploaded_file is not None and analyze_button:

    with st.spinner("Analyzing the image ..."):
        # Encode the image
        base64_image = encode_image(uploaded_file)

        # Determine which prompt to use based on the complexity of the image
        if complex_image:
            prompt_text = complex_image_prompt_text
        else:
            prompt_text = (
                "As an expert in image accessibility and alternative text, succinctly describe the image provided in less than 125 characters. "
                "Provide a brief description using not more than 125 characters that convey the essential information conveyed by the image in three or fewer clear and concise sentences for use as alt text. "
                "Skip phrases like 'image of' or 'picture of.' "
                "Your description should form a clear, well-structured, and factual paragraph that avoids bullet points and newlines, focusing on creating a seamless narrative that serves as effective alternative text for accessibility purposes."
            )
    
        if show_details and additional_details:
            prompt_text += (
                f"\n\nAdditional Context Provided by the User:\n{additional_details}"
            )

    
        # Create the payload for the completion request
        messages = [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt_text},
                    {
                        "type": "image_url",
                        "image_url": f"data:image/jpeg;base64,{base64_image}",
                    },
                ],
            }
        ]
    
        # Make the request to the OpenAI API
        try:
            # Without Stream
            
            # response = openai.chat.completions.create(
            #     model="gpt-4-vision-preview", messages=messages, max_tokens=250, stream=False
            # )
    
            # Stream the response
            full_response = ""
            message_placeholder = st.empty()
            for completion in openai.chat.completions.create(
                model="gpt-4-vision-preview", messages=messages, 
                max_tokens=250, stream=True
            ):
            #     # Check if there is content to display
            #     if completion.choices[0].delta.content is not None:
            #         full_response += completion.choices[0].delta.content
            #         message_placeholder.markdown(full_response + "▌")
            # # Final update to placeholder after the stream ends
            # message_placeholder.markdown(full_response) # stream text
            
                # Check if there is content to display
                if completion.choices[0].delta.content is not None:
                    full_response += completion.choices[0].delta.content

            # Display the response in a text area
            st.text_area('Response:', value=full_response, height=400, key="response_text_area")
            
            st.success('Powered by MTSS GPT. AI can make mistakes. Consider checking important information.')
        except Exception as e:
            st.error(f"An error occurred: {e}")
else:
    # Warnings for user action required
    if not uploaded_file and analyze_button:
        st.warning("Please upload an image.")