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import streamlit as st
import base64
from huggingface_hub import InferenceClient
# 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="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')
# Initialize the Hugging Face InferenceClient with the API key from Streamlit secrets
client = InferenceClient(api_key=st.secrets["huggingface_api_key"])
# File uploader
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
if uploaded_file:
# Display the uploaded image with specified width
image_width = 200 # 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.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:",
disabled=not show_details
)
# Toggle for modifying the prompt for complex images
complex_image = st.checkbox("Is this a complex image?", value=False)
if complex_image:
# Caption explaining the impact of the complex image toggle
st.caption(
"By clicking this toggle, it will instruct the app to create a description that exceeds 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", 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 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 analyze 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 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."
)
if show_details and additional_details:
prompt_text += (
f"\n\nInclude the additional context provided by the user in your description:\n{additional_details}"
)
# Create the payload for the completion request
messages = [
{
"role": "user",
"content": [
{"type": "text", "text": prompt_text},
{
"type": "image",
"image": {
# Provide the image bytes directly
"bytes": base64.b64decode(base64_image)
},
},
],
}
]
# Make the request to the Hugging Face API
try:
# Send the request to the model
completion = client.chat_completions(
model="meta-llama/Llama-3.2-11B-Vision-Instruct",
messages=messages,
max_new_tokens=1200
)
# Extract the assistant's response
assistant_response = completion.get("choices")[0]["message"]["content"]
# Display the response
st.markdown(assistant_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:
# Warning for user action required
if not uploaded_file and analyze_button:
st.warning("Please upload an image.") |