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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"
# Initialize Annif client (no arguments)
annif = AnnifClient()
def get_caption(image):
# 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": "What is in this image?"},
{
"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
PROJECT_ID = "yso-en" # Placeholder, update as needed
def get_subjects(caption):
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):
caption = get_caption(image)
subjects = get_subjects(caption)
return image, caption, subjects
demo = gr.Interface(
fn=lambda image: process_image(image)[1:], # Only return caption and subjects
inputs=gr.Image(type="pil", label="Upload or take a photo"),
outputs=[
gr.Textbox(label="Caption"),
gr.Label(label="Subject Suggestions", show_heading=False),
],
title="VLM Caption & Annif Subject Demo",
description="Upload or take a photo. The app generates a caption using a Visual Language Model and suggests subjects using Annif.",
)
demo.launch()
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