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('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 image with a specified width
st.image('MTSS.ai_Logo.png', width=300)  # Adjusted for consistency

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"]

# Move the file uploader to the sidebar
uploaded_file = st.sidebar.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])

# Initialize placeholders for additional details and complex image details
additional_details = ""
complex_image_details = ""

# Define toggles and input areas for additional details in the main area, not the sidebar
show_details = st.toggle("Add details about the image.", value=False)
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:"
    )

complex_image = st.toggle("Is this a complex image?", value=False)

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

# Always visible response area initialized with a placeholder message
response_placeholder = st.empty()

# Display the uploaded image in an expander if an image is uploaded
if uploaded_file:
    with st.expander("Uploaded Image Preview"):
        st.image(uploaded_file, caption="Uploaded Image", width=300)  # Display uploaded image with adjusted width

# Ensure that analysis only proceeds when an image is uploaded and the analyze button is pressed
if uploaded_file and analyze_button:
    with st.spinner("Analyzing the image..."):
        # Encode the image for analysis
        base64_image = encode_image(uploaded_file)

        # Here you would include your logic to determine the prompt based on the complexity of the image
        # and to generate the full_response using the OpenAI API
        
        # For demonstration, let's simulate a response
        full_response = "This is where the analyzed text will be displayed."

        # Update the response text area with the new full_response
        response_placeholder.text_area('Response:', value=full_response, height=250, key="response_text_area")

        st.success('Analysis complete. Review the generated text for accuracy.')
else:
    # If no image is uploaded, show a waiting message or keep it empty
    response_placeholder.text_area('Response:', value="Upload an image to analyze.", height=250, key="response_text_area")
    if analyze_button:
        st.warning("Please upload an image first.")