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import streamlit as st
import base64
# from openai import OpenAI
import openai
# Function to encode the image to base64
def encode_image(image_file):
return base64.b64encode(image_file.getvalue()).decode("utf-8")
st.set_page_config(page_title="MTSS Image Accessibility Alt Text Generator", layout="centered", initial_sidebar_state="collapsed")
# Streamlit page setup
st.title("MTSS Snapshot: Accessibility Image Textifier: `Alt Text`")
# Retrieve the OpenAI API key from Streamlit secrets
openai_api_key = st.secrets["openai_api_key"]
# Initialize the OpenAI client with the API key
# client = OpenAI(api_key=openai_api_key)
client = openai.api_key(api_key=openai_api_key)
# openai.api_key = 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 st.expander("Image", expanded = True):
st.image(uploaded_file, caption=uploaded_file.name, use_column_width=True)
# 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(
"Add any additional details or context about the image here:",
disabled=not show_details
)
# Button to trigger the analysis
analyze_button = st.button("Analyse the Scientific Image", type="secondary")
# 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 api_key and analyze_button:
with st.spinner("Analysing the image ..."):
# Encode the image
base64_image = encode_image(uploaded_file)
# Optimized prompt for additional clarity and detail
prompt_text = (
"You are a highly knowledgeable accessibility specialist. "
"Your task is to examine the following image in detail. "
"Provide a comprehensive, factual, and accurate explanation of what the image depicts. "
"Highlight key elements and their significance, and present your analysis in clear, well-structured format. "
"Assume the reader has a basic understanding of scientific concepts."
"Create a detailed image caption using 150 words or less."
)
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 = client.chat.completions.create(
# model="gpt-4-vision-preview", messages=messages, max_tokens=500, stream=False
# )
# Stream the response
full_response = ""
message_placeholder = st.empty()
for completion in client.chat.completions.create(
model="gpt-4-vision-preview", messages=messages,
max_tokens=1200, 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)
# Display the response in the app
# st.write(response.choices[0].message.content)
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.")
if not api_key:
st.warning("Please enter your OpenAI API key.") |