app.py

import os
import time
import base64
import requests
import streamlit as st

# Access the secret API key
# if the app is running locally, you can set the API key as an environment variable
api_key = os.getenv("NVIDIA_APP_KEY")

# Function to encode the image
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

# stream the response
stream = True

headers = {
  "Authorization": f"Bearer {api_key}",
  "Accept": "text/event-stream" if stream else "application/json"
}
  
def main():
    st.title("Multimodal using GPT 4 Turbo Model")

    text = """Prof. Louie F. Cervantes, M. Eng. (Information Engineering)
    CCS 229 - Intelligent Systems
    Department of Computer Science
    College of Information and Communications Technology
    West Visayas State University
    """
    with st.expander("About"):
        st.text(text)

    st.write("Upload an image and select the image analysis task.")

    # File upload for image
    uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
    if uploaded_image is not None:
        # Encode the uploaded image to base64
        base64_image = base64.b64encode(uploaded_image.getvalue()).decode('utf-8')

        # Display the uploaded image
        st.image(uploaded_image, caption="Uploaded Image",  use_container_width=True)

    # List of image analysis tasks
    analysis_tasks = [
        "Scene Analysis: Describe the scene depicted in the image. Identify the objects present, their spatial relationships, and any actions taking place.",
        "Object Detection and Classification: Identify and classify all objects present in the image. Provide detailed descriptions of each object, including its size, shape, color, and texture.",
        "Image Captioning: Generate a concise and accurate caption that describes the content of the image.",
        "Visual Question Answering: Answer specific questions about the image, such as 'What color is the car?' or 'How many people are in the image?'",
        "Image Similarity Search: Given a query image, find similar images from a large dataset based on visual features.",
        "Image Segmentation: Segment the image into different regions corresponding to objects or areas of interest.",
        "Optical Character Recognition (OCR): Extract text from the image, such as printed or handwritten text.",
        "Diagram Understanding: Analyze a diagram (e.g., flowchart, circuit diagram) and extract its structure and meaning.",
        "Art Analysis: Describe the artistic style, subject matter, and emotional impact of an image.",
        "Medical Image Analysis: Analyze medical images (e.g., X-rays, MRIs) to detect abnormalities or diagnose diseases."
    ]

    # Task selection dropdown
    selected_task = st.selectbox("Select an image analysis task:", analysis_tasks)

    # Button to generate response
    if st.button("Generate Response"):
        if uploaded_image is None or selected_task == "":
            st.error("Please upload an image and sekect a task.")
        else:
            # Prepare the multimodal prompt
            payload = {
            "model": 'meta/llama-3.2-90b-vision-instruct',
            "messages": [
                {
                "role": "user",
                "content": f'{selected_task} <img src="data:image/png;base64,{base64_image}" />'
                }
            ],
            "max_tokens": 512,
            "temperature": 1.00,
            "top_p": 1.00,
            "stream": stream
            }

            with st.spinner("Processing..."):
                try:
                    # Generate response
                    response = requests.post("https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct/chat/completions", headers=headers, json=payload)
                    # Display the response if streaming                
                    if stream:
                        for line in response.iter_lines():
                            if line:
                                st.write(line.decode("utf-8"))
                    else:                
                        # Show the response content
                        content = response.json()
                        contentstring = content['choices'][0]['message']['content']
                        st.write(f"AI Response: {contentstring}")
                    st.success("Response generated!")
                except Exception as e:
                    st.error(f"An error occurred: {e}")

if __name__ == "__main__":
    main()