| import gradio as gr |
| from predict import ONNXInference |
| import json |
|
|
| def detect(files): |
| model = ONNXInference( |
| model_path="./torchFlow-ckpt.onnx", |
| files=files, |
| save_image=False, |
| save_path="./" |
| ) |
| res = model.run() |
| |
| |
| |
| |
|
|
| result = json.dumps(res) |
| return result |
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("# DetectIt") |
| with gr.Row(): |
| image = gr.UploadButton( |
| label="Upload Image", |
| file_types=[".jpg",".jpeg"], |
| file_count="multiple") |
|
|
| btn = gr.Button("Go") |
| text = gr.Textbox(show_label=False, elem_id="result-textarea") |
| btn.click(detect, inputs=[image], outputs=[text], api_name="predict") |
|
|
| demo.launch() |
