adding demo file

.gitignore

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+# --- Sensitive Information ---
+# Ignore environment files containing secrets like API keys or tokens.
+.env
+*.env
+.env.*
+
+# --- Python Virtual Environments ---
+# A virtual environment should not be committed.
+# Instead, commit a requirements.txt file.
+/venv/
+/env/
+/.venv/
+/.gradio/
+/ENV/
+
+# --- Python Artifacts ---
+# Ignore Python cache and compiled files.
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+*.egg-info/
+dist/
+build/
+
+# --- Models & Data ---
+# Ignore large model files. These should be downloaded or loaded from a remote source.
+*.onnx
+*.pt
+*.pth
+*.h5
+
+# Ignore datasets. These are typically too large for version control.
+# You can uncomment the line below if your data is in a 'data/' folder.
+# /data/
+
+# --- IDE & Editor Configuration ---
+# Ignore configuration files from common editors like VSCode and PyCharm.
+.vscode/
+.idea/
+
+# --- Jupyter Notebook Checkpoints ---
+.ipynb_checkpoints/
+
+# --- Operating System Files ---
+# Ignore OS-specific files.
+.DS_Store
+Thumbs.db
+
+# --- Log Files ---
+# Ignore log files, which can become large and are specific to a run.
+*.log

README.md

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+# 2025-iskyn
+
+Minimal structure for running ONNX image inference with a simple CLI.
+
+## Setup
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+```
+
+## Predict with ONNX
+
+```bash
+python main.py --model best.onnx --image path/to/image.jpg --output out.png
+```
+
+`detect_objects` currently returns the input image unchanged. Implement your model-specific post-processing in `src/iskyn/inference/onnx_runtime.py`.
+
+

main.py

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+import numpy as np
+import cv2
+import gradio as gr
+from ultralytics import YOLO
+import os
+from huggingface_hub import hf_hub_download
+from dotenv import load_dotenv
+
+
+print("Loading environment variables...")
+load_dotenv()
+
+HF_REPO_ID = "tententgc/Iskyn"
+MODEL_FILENAME = "best.onnx"
+
+print(f"Downloading '{MODEL_FILENAME}' from '{HF_REPO_ID}'...")
+
+model_path = hf_hub_download(
+    repo_id=HF_REPO_ID,
+    filename=MODEL_FILENAME,
+    token=os.getenv("HF_TOKEN") 
+)
+
+print(f"Model downloaded to: {model_path}")
+
+
+
+print("Loading YOLO model...")
+onnx_model = YOLO(model_path) 
+print("Model loaded successfully.")
+
+
+
+def predict_image(image_filepath, conf_threshold, iou_threshold):
+    results = onnx_model.predict(
+        image_filepath,
+        conf=conf_threshold,
+        iou=iou_threshold
+    )
+    result = results[0]
+    im_array = result.plot()
+    im_rgb = cv2.cvtColor(im_array, cv2.COLOR_BGR2RGB)
+    return im_rgb
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=[
+        gr.Image(type="filepath", label="Upload Image"),
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence Threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU Threshold")
+    ],
+    outputs=gr.Image(type="numpy", label="Result"),
+    title="Detection Face Skin",
+    description="Upload an image and adjust the thresholds to fine-tune detection." 
+)
+
+iface.launch()

requirements.txt

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+onnxruntime>=1.18.0
+numpy>=1.26.0
+opencv-python>=4.9.0.80
+Pillow>=10.3.0
+gradio>=4.44.0
+