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ui/app.py — Gradio layout.
This file is pure layout — no inference logic lives here.
All intelligence is in core/api_client.py.
"""
import gradio as gr
from core import stream_description
from ui.components import (
image_upload,
prompt_box,
model_selector,
generation_settings,
api_key_field,
prompt_examples,
output_box,
info_panel,
)
CSS = """
#title { text-align: center; }
#submit { background: #6366f1; color: white; }
#submit:hover { background: #4f46e5; }
#model-hint { font-size: 0.85em; opacity: 0.75; }
footer { display: none !important; }
"""
def build_ui() -> gr.Blocks:
with gr.Blocks(
title="MiniCPM-V · API Image Describer",
theme=gr.themes.Soft(),
css=CSS,
) as demo:
gr.Markdown(
"# 🔍 MiniCPM-V 4.6 · Image Description via API\n"
"**No downloads. No GPU needed.** Calls OpenBMB's hosted API — "
"results stream back in real time.\n\n"
"> Models: `MiniCPM-V-4.6-Instruct` & `MiniCPM-V-4.6-Thinking` · "
"1.3 B params each · well under the 32 B cap · Apache-2.0",
elem_id="title",
)
with gr.Row():
# ── Left column — inputs ──────────────────────────────────────────
with gr.Column(scale=1):
image = image_upload()
prompt = prompt_box()
model = model_selector()
gr.Markdown(
"**Instruct** — direct, concise answer. \n"
"**Thinking** — reasons step-by-step first (slower, more thorough).",
elem_id="model-hint",
)
max_tokens, temperature = generation_settings()
api_key = api_key_field()
submit = gr.Button(
"▶ Describe Image", variant="primary", elem_id="submit"
)
prompt_examples(prompt)
# ── Right column — output ─────────────────────────────────────────
with gr.Column(scale=1):
output = output_box()
info_panel()
# ── Event wiring ──────────────────────────────────────────────────────
inputs = [image, prompt, model, max_tokens, temperature, api_key]
outputs = [output]
submit.click(fn=stream_description, inputs=inputs, outputs=outputs)
prompt.submit(fn=stream_description, inputs=inputs, outputs=outputs)
return demo
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