app.py

import os
import base64
import requests
import streamlit as st
import json
import tempfile

if "stream" not in st.session_state:
    st.session_state.stream = True

api_key = os.getenv("NVIDIA_VISION_API_KEY")
MODEL_ID = "meta/llama-3.2-90b-vision-instruct"
invoke_url = "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct/chat/completions"

# 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')

def main():
    st.title(f"Multimodal Image Analysis with {MODEL_ID}")

    # Display about section
    about_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(about_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"])
    temp_file_path = None

    if uploaded_image is not None:
        with tempfile.NamedTemporaryFile(delete=False) as temp_file:
            temp_file.write(uploaded_image.getvalue())
            temp_file_path = temp_file.name

        # Encode image as Base64
        with open(temp_file_path, "rb") as f:
            base64_image = base64.b64encode(f.read()).decode()

        # 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)

    # Checkbox for streaming
    stream = st.checkbox("Begin streaming the AI response as soon as it is available.", value=st.session_state.stream)

    if st.button("Generate Response"):
        if not api_key:
            st.error("API key not found. Please set the NVIDIA_VISION_API_KEY environment variable.")
            return

        if uploaded_image is None:
            st.error("Please upload an image.")
            return

        if not selected_task:
            st.error("Please select an image analysis task.")
            return

        # Headers for the API call
        headers = {
            "Authorization": f"Bearer {api_key}",
            "Accept": "text/event-stream" if stream else "application/json"
        }

        # Prepare the multimodal prompt
        payload = {
            "model": MODEL_ID,
            "messages": [
                {
                    "role": "user",
                    "content": f'{selected_task} <img src="data:image/png;base64,{base64_image}" />'
                }
            ],
            "max_tokens": 512,
            "temperature": 1.0,
            "top_p": 1.0,
            "stream": stream
        }

        try:
            with st.spinner("Processing..."):
                response = requests.post(
                    invoke_url,
                    headers=headers,
                    json=payload,
                    stream=stream
                )
                response.raise_for_status()  # Raise exception for HTTP errors

                if stream:
                    # Handle streaming response
                    response_container = st.empty()
                    content = ""
                    for chunk in response.iter_lines(decode_unicode=True):
                        if chunk:
                            if "[DONE]" in chunk:
                                # Handle the end chunk
                                st.write("Response generation complete.")
                                break                            
                            # Check if the chunk is a JSON string
                            elif chunk.startswith("data:"):
                                chunk = chunk[5:].strip()  # Remove the "data:" prefix
                                try:
                                    if len(chunk) > 0:
                                        chunk_dict = json.loads(chunk)
                                        if "choices" in chunk_dict and chunk_dict["choices"]:
                                            delta_content = chunk_dict["choices"][0]["delta"]["content"]
                                            content += delta_content
                                            response_container.write(content)

                                except json.JSONDecodeError as e:
                                    st.error(f"Error parsing JSON: {e}")

                else:
                    # Handle non-streaming response
                    content = response.json()
                    content_string = content.get("choices", [{}])[0].get("message", {}).get("content", "")
                    st.write(f"AI Response: {content_string}")
                    st.success("Response generated!")
        except requests.exceptions.RequestException as e:
            st.error(f"An error occurred while making the API call: {e}")
        except Exception as e:
            st.error(f"An unexpected error occurred: {e}")
        finally:
            # Clean up temporary file
            if temp_file_path and os.path.exists(temp_file_path):
                os.remove(temp_file_path)

if __name__ == "__main__":
    main()