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98d37aa f6b8d9d 98d37aa f6b8d9d 98d37aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | """Fuse-MD Hugging Face ZeroGPU demo.
The Space must also contain the ``src`` and ``api`` folders and the selected
checkpoint at the repository root. The large text backbone is downloaded from
the Hugging Face Hub during startup.
"""
import os
import sys
import time
from pathlib import Path
try:
import spaces
GPU = spaces.GPU
except ImportError: # pragma: no cover - local-only fallback
def GPU(*args, **kwargs):
def decorate(function):
return function
return decorate
import gradio as gr
from huggingface_hub import snapshot_download
ROOT = Path(__file__).resolve().parent
sys.path.insert(0, str(ROOT))
TEXT_MODEL = "VishnuPJ/MalayaLLM_7B_Base"
CHECKPOINT = ROOT / "custom_tamil_llamavit_fusion_element.pth"
MODEL_DIR = ROOT / ".hf_model_cache" / "VishnuPJ__MalayaLLM_7B_Base"
# Download one text backbone only. Keeping a single language model reduces
# startup time and storage usage for the free demo.
if not MODEL_DIR.exists():
snapshot_download(repo_id=TEXT_MODEL, local_dir=MODEL_DIR)
os.environ.setdefault("FUSEMD_CHECKPOINT", str(CHECKPOINT))
os.environ.setdefault("FUSEMD_LOCAL_MODEL_ROOT", str(ROOT / ".hf_model_cache"))
os.environ.setdefault("FUSEMD_DEVICE", "cuda")
from api.api.config import settings # noqa: E402
from api.api.service import FuseMDService # noqa: E402
service = FuseMDService(settings)
service.load()
@GPU(duration=120)
def predict(image_path, text, threshold):
if image_path is None:
return "Please upload a meme image."
started = time.perf_counter()
with open(image_path, "rb") as image_file:
response = service.predict(
text=text or "",
image_bytes=image_file.read(),
image_filename=Path(image_path).name,
threshold=threshold,
)
elapsed = time.perf_counter() - started
return (
f"### Prediction: **{response['label']}**\n\n"
f"Positive probability: **{response['probability']:.1%}**\n\n"
f"Threshold: {response['threshold']:.2f} | "
f"Language: {response['language']} | Fusion: {response['fusion_method']}\n\n"
f"Runtime: **{elapsed:.1f} seconds**"
)
with gr.Blocks(title="Fuse-MD Demo") as demo:
gr.Markdown(
"# Fuse-MD\n"
"Upload a Tamil meme and enter its transcription. The model predicts "
"whether it contains misogynistic content."
)
with gr.Row():
image = gr.Image(type="filepath", label="Meme image", width=420, height=420)
text = gr.Textbox(label="Meme transcription", lines=5)
threshold = gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="Decision threshold")
button = gr.Button("Predict", variant="primary")
result = gr.Markdown()
button.click(predict, inputs=[image, text, threshold], outputs=result)
demo.queue().launch()
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