| import gradio as gr | |
| from predict import ONNXInference | |
| PRED = [] | |
| def detect(files): | |
| model = ONNXInference( | |
| model_path="/home/neo/Downloads/torchFlow/models/torchFlow-ckpt.onnx", | |
| files=files, | |
| save_image=False, | |
| save_path="/home/neo/Downloads/torchFlow/" | |
| ) | |
| res = model.run() | |
| img_id = res["IMG_ID"] | |
| pred_lab = res["PRED_LAB"], | |
| pred_ct = res["PRED_CT"], | |
| geo_tag_url = res["GEO_TAG_URL"] | |
| PRED.append(pred_ct) | |
| return f"Predicted" | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| output=gr.Image() | |
| with gr.Row(): | |
| btn = gr.UploadButton( | |
| label="Upload Image", | |
| file_types = ['.jpg','.jpeg'], | |
| file_count = "multiple") | |
| btn.upload(fn=detect, inputs=btn) | |
| with gr.Column(scale=1, min_width=600): | |
| gr.Markdown(f"Output here") | |
| if PRED is not None: | |
| gr.Markdown(f"Predicted: {PRED}") | |
| demo.launch() |