app.py

import streamlit as st
import requests

# Hugging Face API setup
API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-3.2-11B-Vision-Instruct"
headers = {"Authorization": f"Bearer {st.secrets['huggingface_api_key']}"}

# Function to query the model
def query_image(image_data, prompt_text):
    # Prepare the payload
    payload = {
        "inputs": {
            "image": image_data,
            "text": prompt_text
        }
    }
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

# 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  # Desired width in pixels
st.image('MTSS.ai_Logo.png', width=image_width)

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

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

if uploaded_file:
    # Display the uploaded image
    image_width = 200  # 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.checkbox("Add details about the image.", value=False)

if show_details:
    # Text input for additional details about the image
    additional_details = st.text_area(
        "Include specific information important for the alt text or reflect why the image is being used:"
    )

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

if complex_image:
    st.caption(
        "By selecting this, the app will create a description exceeding the 125-character limit. "
        "Add the description in a placeholder behind the image and 'Description in the content placeholder' in the alt text box."
    )

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

# 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 and if the button has been pressed
if uploaded_file is not None and analyze_button:

    with st.spinner("Analyzing the image ..."):
        # Read the image file
        image_bytes = uploaded_file.read()

        # 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\nInclude the additional context provided by the user in your description:\n{additional_details}"
            )

        # Query the model
        try:
            response = query_image(image_bytes, prompt_text)

            # Extract the generated text from the response
            if isinstance(response, dict) and 'generated_text' in response:
                alt_text = response['generated_text']
            elif isinstance(response, list) and 'generated_text' in response[0]:
                alt_text = response[0]['generated_text']
            else:
                alt_text = "No description generated."

            # Display the generated alt text
            st.markdown(f"**Generated Alt Text:** {alt_text}")
            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:
    # Warning for user action required
    if not uploaded_file and analyze_button:
        st.warning("Please upload an image.")