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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")
# #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('MTSS:grey[.ai]')
# st.header('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 = 100 # 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)
# 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
# )
# # Toggle for modifying the prompt for complex images
# complex_image = st.toggle("Is this a complex image? ", value=False)
# if complex_image:
# # Text input for additional details about the image, shown only if toggle is True
# complex_image_details = st.caption(
# "By clicking this toggle, it will inform MTSS.ai 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 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=250, 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.")
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 logo 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('VisionText™ | Accessibility')
st.subheader(':green[_Image Alt Text Generator_]')
# Retrieve the OpenAI API Key from secrets
openai.api_key = st.secrets["openai_api_key"]
# Initialize an empty string for the full_response to ensure the text area is always displayed
full_response = "Awaiting analysis..."
# Toggle for showing additional details input
show_details = st.toggle("Add details about the image.", value=False)
# Text input for additional details about the image, shown based on the toggle state
additional_details = ""
if show_details:
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:"
)
# Toggle for modifying the prompt for complex images
complex_image = st.toggle("Is this a complex image?", value=False)
# Button to trigger the analysis
analyze_button = st.button("Analyze the Image")
# Display the response in a text area
response_text_area = st.text_area('Response:', value=full_response, height=250, key="response_text_area")
# 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 in an expander
with st.expander("Image Preview", expanded=True):
st.image(uploaded_file, caption=uploaded_file.name, width=100, use_column_width=False)
# Ensure that analysis only proceeds when an image is uploaded and the analyze button is pressed
if uploaded_file is not None and analyze_button:
with st.spinner("Analyzing the image..."):
# Encode the image for analysis
base64_image = encode_image(uploaded_file)
# Logic to set prompt_text based on complex_image toggle state and append additional_details if provided
# Your OpenAI API call and handling logic here to update full_response based on the analysis
# Update the response text area with the new full_response
response_text_area.text_area('Response:', value=full_response, height=250, key="response_text_area")
st.success('Analysis complete. Review the generated text for accuracy.')
else:
if not uploaded_file and analyze_button:
st.warning("Please upload an image to proceed with the analysis.")
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