Resolve merge conflicts and continue rebase

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,5 +1,5 @@
 ---
-title: Samewind
+title: SpecDETR
 emoji: 🔥
 colorFrom: purple
 colorTo: pink
@@ -9,4 +9,22 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SpecDETR Hugging Face Space
+
+This repository contains a Gradio demo for SpecDETR, supporting both image and video detection. 
+
+## Usage
+- Upload your model config (.py) and checkpoint (.pth) files to the interface.
+- Test detection on images or videos using the Gradio UI.
+
+## Files
+- `app.py`: Gradio app entry point.
+- `requirements.txt`: Python dependencies for Hugging Face Spaces.
+- `download_spod.py`: (Optional) Script for dataset download and config update.
+
+## Deployment
+1. Push this repo to your Hugging Face Space.
+2. The Space will auto-build and launch the Gradio demo.
+
+## Note
+- If you need to add default configs or checkpoints, place them in the repo and update `app.py` accordingly.

app.py

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+import gradio as gr
+import os
+import tempfile
+import cv2
+import numpy as np
+from mmdet.apis import DetInferencer
+
+# Helper to load model
+inferencer = None
+def load_model(config_path, checkpoint_path):
+    global inferencer
+    inferencer = DetInferencer(model=config_path, weights=checkpoint_path)
+    return "Model loaded."
+
+def infer_image(image):
+    if inferencer is None:
+        return "Please load a model first.", None
+    result = inferencer(image)
+    vis = result["visualization"]
+    if isinstance(vis, list):
+        vis = vis[0]
+    return "", vis
+
+def infer_video(video):
+    if inferencer is None:
+        return "Please load a model first.", None
+    temp_dir = tempfile.mkdtemp()
+    cap = cv2.VideoCapture(video)
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    out_path = os.path.join(temp_dir, "result.mp4")
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter(out_path, fourcc, fps, (w, h))
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        result = inferencer(frame)
+        vis = result["visualization"]
+        if isinstance(vis, list):
+            vis = vis[0]
+        out.write(vis[:,:,::-1])
+    cap.release()
+    out.release()
+    return "", out_path
+
+def ui():
+    with gr.Blocks() as demo:
+        gr.Markdown("# SpecDETR Demo: Image and Video Detection\nUpload your config (.py) and checkpoint (.pth) to start.")
+        with gr.Row():
+            config = gr.File(label="Config File (.py)")
+            checkpoint = gr.File(label="Checkpoint (.pth)")
+            load_btn = gr.Button("Load Model")
+            load_status = gr.Textbox(label="Status", interactive=False)
+        load_btn.click(load_model, inputs=[config, checkpoint], outputs=load_status)
+        with gr.Tab("Image"):
+            img_input = gr.Image(type="numpy")
+            img_output = gr.Image()
+            img_btn = gr.Button("Detect on Image")
+            img_status = gr.Textbox(label="Status", interactive=False)
+            img_btn.click(infer_image, inputs=img_input, outputs=[img_status, img_output])
+        with gr.Tab("Video"):
+            vid_input = gr.Video()
+            vid_output = gr.Video()
+            vid_btn = gr.Button("Detect on Video")
+            vid_status = gr.Textbox(label="Status", interactive=False)
+            vid_btn.click(infer_video, inputs=vid_input, outputs=[vid_status, vid_output])
+    return demo
+
+demo = ui()
+
+def main():
+    demo.launch()
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,5 @@
+gradio>=3.0.0
+opencv-python
+mmdet
+mmcv
+numpy