app.py

import streamlit as st
from transformers import pipeline
from huggingface_hub import InferenceClient
from PIL import Image
import base64

# Streamlit page setup
st.set_page_config(page_title="MTSS Image Accessibility Alt Text Generator", layout="centered", initial_sidebar_state="auto")

# 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.header('VisionTexts™ | Accessibility')
st.subheader('Image Alt Text Creator')

# Retrieve the Hugging Face API Key from secrets
huggingface_api_key = st.secrets["huggingface_api_key"]

# Initialize the image captioning pipeline
image_captioner = pipeline(
    "image-to-text",
    model="Salesforce/blip-image-captioning-large",
    use_auth_token=huggingface_api_key
)

# Initialize the language model client
client = InferenceClient(token=huggingface_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
    image = Image.open(uploaded_file)
    image_width = 200  # Set the desired width in pixels
    with st.expander("Image", expanded=True):
        st.image(image, caption=uploaded_file.name, width=image_width, use_column_width=False)
else:
    st.warning("Please upload an image.")

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

if show_details:
    # Text input for additional details about the image
    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:"
    )
else:
    additional_details = ""

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

# 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 caption provided. "
    "Provide a brief description using not more than 500 characters that conveys the essential information 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 and if the button has been pressed
if uploaded_file is not None and analyze_button:
    with st.spinner("Analyzing the image..."):
        # Get the caption from the image using the image captioning model
        caption_response = image_captioner(image)
        image_caption = caption_response[0]['generated_text']

        # 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 caption provided in less than 125 characters. "
                "Provide a brief description using not more than 125 characters that conveys the essential information 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 for accessibility purposes."
            )

        # Include additional details if provided
        if additional_details:
            prompt_text += f"\n\nInclude the additional context provided by the user in your description:\n{additional_details}"

        # Create the prompt for the language model
        full_prompt = f"{prompt_text}\n\nImage Caption: {image_caption}"

        # Prepare messages for chat interface
        messages = [
            {"role": "user", "content": full_prompt}
        ]

        # Use the language model to generate the alt text description
        try:
            # Stream the response from the language model
            stream = client.chat(
                model="meta-llama/Llama-2-7b-chat-hf",
                messages=messages,
                stream=True
            )

            # Stream the response
            full_response = ""
            message_placeholder = st.empty()
            for chunk in stream:
                if 'generated_text' in chunk:
                    content = chunk['generated_text']
                    full_response += content
                    message_placeholder.markdown(full_response + "▌")
            # Final update after stream ends
            message_placeholder.markdown(full_response)

            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:
    st.write("Please upload an image and click 'Analyze the Image' to generate alt text.")