app.py

import gradio as gr
import requests
from annif_client import AnnifClient
import os


# Get VLM API base URL and API key from environment variables
VLM_API_BASE_URL = os.getenv("VLM_API_BASE_URL")
if not VLM_API_BASE_URL:
    raise RuntimeError("VLM_API_BASE_URL environment variable must be set.")
VLM_API_KEY = os.getenv("VLM_API_KEY", "")
VLM_API_ENDPOINT = f"{VLM_API_BASE_URL}/v1/chat/completions"


# Get Annif API base URL from environment variable, fallback to default
ANNIF_API_BASE_URL = os.getenv("ANNIF_API_BASE_URL")
if ANNIF_API_BASE_URL:
    if not ANNIF_API_BASE_URL.endswith("v1/"):
        raise RuntimeError("ANNIF_API_BASE_URL should end with 'v1/'")
    annif = AnnifClient(api_base=ANNIF_API_BASE_URL)
else:
    annif = AnnifClient()


def get_caption(image, prompt):
    # Convert image to base64 JPEG
    import io
    import base64

    buf = io.BytesIO()
    image.save(buf, format="JPEG")
    img_bytes = buf.getvalue()
    img_b64 = base64.b64encode(img_bytes).decode("utf-8")

    # Prepare payload for VLM (OpenAI schema)
    payload = {
        "messages": [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {
                        "type": "image_url",
                        "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"},
                    },
                ],
            }
        ],
        "max_tokens": 300,
    }
    headers = {"X-API-Key": VLM_API_KEY} if VLM_API_KEY else {}
    try:
        response = requests.post(VLM_API_ENDPOINT, json=payload, headers=headers)
        response.raise_for_status()
        data = response.json()
        # Assume caption is in data['choices'][0]['message']['content']
        caption = data["choices"][0]["message"]["content"]
    except Exception as e:
        print(f"VLM API error: {e}")  # Detailed error for admin
        raise gr.Error("Sorry, there was a problem generating a caption.")
    return caption


def get_subjects(caption, project_id):
    try:
        results = annif.suggest(project_id=project_id, text=caption)
        label_scores = {result["label"]: result["score"] for result in results}
        if not label_scores:
            return {}
        return label_scores
    except Exception as e:
        print(f"Annif API error: {e}")  # Detailed error for admin
        raise gr.Error("Sorry, there was a problem getting subject suggestions.")


def process_image(image, project_id):
    prompt = (
        "Generate an alt-text description, which is a description for people who can't see the image. "
        "Be sure to talk about the actual contents of it, do not interpret anything. "
        "Start with a general description, then focus on details. Answer only with the "
        "alt-text description, do not include 'Here's an alt-text description', explanations or subheadings."
    )
    caption = get_caption(image, prompt)
    subjects = get_subjects(caption, project_id)
    return image, caption, subjects


with gr.Blocks(title="VLM Caption & Annif Subject Demo") as demo:
    gr.Markdown("# VLM Caption & Annif Subject Demo")
    gr.Markdown(
        """
    **How it works:**
    1. Upload or take a photo in the input section below.
    2. The image is sent to a Visual Language Model to generate a caption.
    3. Annif suggests subjects based on the caption.
    """
    )
    with gr.Row():
        with gr.Column():
            gr.Markdown("### Input")
            image_input = gr.Image(
                type="pil", label="Image Input (upload or take a photo)"
            )
            project_dropdown = gr.Dropdown(
                choices=[("YSO", "yso-en"), ("YKL", "ykl-en")],
                value="yso-en",
                label="Annif Project",
                info="Select the vocabulary from where subject suggestions are drawn",
            )
            submit_btn = gr.Button("Submit", interactive=False)
            clear_btn = gr.Button("Clear")
        with gr.Column():
            gr.Markdown("### Output")
            caption_output = gr.Textbox(label="Caption", lines=10, interactive=False)
            subjects_output = gr.Label(label="Subject Suggestions", show_heading=False)

    def run_app(image, project_id):
        caption, subjects = process_image(image, project_id)[1:]
        return caption, subjects

    submit_btn.click(
        run_app,
        inputs=[image_input, project_dropdown],
        outputs=[caption_output, subjects_output],
    )
    clear_btn.click(lambda: ("", {}), outputs=[caption_output, subjects_output])

    def update_submit_btn(img):
        return gr.update(interactive=img is not None)

    image_input.upload(update_submit_btn, inputs=image_input, outputs=submit_btn)

demo.launch()