Create app.py

app.py

@@ -0,0 +1,74 @@
+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+import tempfile
+import os
+
+def detect(weights_file, image_file, conf_threshold):
+    if weights_file is None or image_file is None:
+        return None, "Please upload both a .pt weights file and an image."
+
+    # Save uploaded weights to a temp path
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".pt") as tmp_w:
+        tmp_w.write(weights_file.read())
+        weights_path = tmp_w.name
+
+    # Load model from uploaded weights
+    model = YOLO(weights_path)
+
+    # Load input image as PIL
+    if isinstance(image_file, str):
+        img = Image.open(image_file).convert("RGB")
+    else:
+        img = Image.open(image_file).convert("RGB")
+
+    # Run prediction; return annotated image
+    results = model.predict(
+        source=img,
+        conf=conf_threshold,
+        imgsz=640,
+        verbose=False,
+    )
+    r = results[0]
+    annotated = r.plot()        # numpy array BGR
+    annotated_pil = Image.fromarray(annotated[:, :, ::-1])  # BGR -> RGB
+
+    return (img, annotated_pil), f"Detections done with {os.path.basename(weights_file.name)}"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# YOLOv8 Viewer\nUpload a YOLO `.pt` weights file and an image. The app will show original and detections side by side.")
+
+    with gr.Row():
+        weights_input = gr.File(
+            label="YOLO weights (.pt)",
+            file_types=[".pt"],
+            type="binary",
+        )
+        conf_slider = gr.Slider(
+            minimum=0.1,
+            maximum=0.9,
+            value=0.25,
+            step=0.05,
+            label="Confidence threshold",
+        )
+
+    image_input = gr.Image(type="filepath", label="Input image")
+
+    gallery = gr.Gallery(
+        label="Original (left) vs Detections (right)",
+        columns=2,
+        height=512,
+    )
+    status = gr.Textbox(label="Status / Info", interactive=False)
+
+    run_btn = gr.Button("Run detection")
+
+    run_btn.click(
+        fn=detect,
+        inputs=[weights_input, image_input, conf_slider],
+        outputs=[gallery, status],
+    )
+
+if __name__ == "__main__":
+    demo.launch()