Update app.py

app.py

@@ -1,77 +1,25 @@
+import sys, os
+sys.path.append(os.path.abspath(os.path.dirname(__file__)))
 import gradio as gr
 import os
-import tempfile
-import cv2
-import numpy as np
-from mmdet.apis import DetInferencer
+from inference_custom import main as samwise_infer
 
-# 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 inference(video, prompt):
+    output_path = "output_segmented.mp4"
+    # 'video' is a file path string provided by Gradio
+    samwise_infer(video, prompt, output_path, "models/samwise.pth")
+    return output_path
 
-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()
+demo = gr.Interface(
+    fn=inference,
+    inputs=[
+        gr.Video(label="Upload Video"),
+        gr.Textbox(label="Text Prompt", placeholder="Describe what to segment")
+    ],
+    outputs=gr.Video(label="Segmented Output"),
+    title="SAMWISE Video Segmentation",
+    description="Upload a video and enter a prompt to segment objects with SAMWISE."
+)
 
 if __name__ == "__main__":
-    main()
+    demo.launch()