Create App2.py

App2.py

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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.")