Update app.py

app.py

@@ -1,138 +1,44 @@
-# from ultralytics import YOLO
-# import cv2
-# import gradio as gr
-# import numpy as np
-
-# # -------------------------
-# # Load models (once)
-# # -------------------------
-# det_model = YOLO(
-#     "models/detect/best_yolov8s.onnx"
-# )
-
-# cls_model = YOLO(
-#     "models/classify/Buck_classification_epoch_26_best.onnx",
-#     task="classify"
-# )
-
-# # -------------------------
-# # Inference function
-# # -------------------------
-# def predict(image):
-#     # Convert RGB (Gradio) → BGR (OpenCV)
-#     image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-
-#     det_results = det_model(image)
-
-#     for r in det_results:
-#         for box in r.boxes:
-#             x1, y1, x2, y2 = map(int, box.xyxy[0])
-
-#             crop = image[y1:y2, x1:x2]
-#             if crop.size == 0:
-#                 continue
-
-#             # Classification
-#             cls_results = cls_model(crop)
-#             probs = cls_results[0].probs
-
-#             cls_id = probs.top1
-#             cls_conf = probs.top1conf
-#             cls_name = cls_results[0].names[cls_id]
-
-#             # Labels
-#             label = f"Deer | {cls_name} ({cls_conf:.2f})"
-
-#             # Draw box + label
-#             cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-#             cv2.putText(
-#                 image,
-#                 label,
-#                 (x1, y1 - 10),
-#                 cv2.FONT_HERSHEY_SIMPLEX,
-#                 0.7,
-#                 (0, 255, 0),
-#                 2
-#             )
-
-#     # Convert back BGR → RGB
-#     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-#     return image
-
-
-# # -------------------------
-# # Gradio UI
-# # -------------------------
-# app = gr.Interface(
-#     fn=predict,
-#     inputs=gr.Image(type="numpy", label="Upload Deer Image"),
-#     outputs=gr.Image(type="numpy", label="Prediction"),
-#     title="Buck Tracker AI – Deer Detection & Classification",
-#     description="Upload a trail cameras image. The system detects deer and classifies Buck/Doe using a multi-stage YOLO pipeline."
-# )
-
-# # -------------------------
-# # Launch
-# # -------------------------
-# if __name__ == "__main__":
-#     app.launch()
-from ultralytics import YOLO
-import cv2
-import gradio as gr
-import numpy as np
-
-# -------------------------
-# Load detection model
-# -------------------------
-det_model = YOLO(r"models\buck_vs_doe_Detection_best.pt")
-
-# -------------------------
-# Inference function
-# -------------------------
-def predict(image):
-    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-
-    results = det_model(image)
-
-    for r in results:
-        for box in r.boxes:
-            x1, y1, x2, y2 = map(int, box.xyxy[0])
-            conf = float(box.conf[0])
-            cls_id = int(box.cls[0])
-
-            # ✅ Auto class name from Ultralytics
-            class_name = det_model.names[cls_id]
-
-            label = f"{class_name} ({conf:.2f})"
-
-            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            cv2.putText(
-                image,
-                label,
-                (x1, y1 - 10),
-                cv2.FONT_HERSHEY_SIMPLEX,
-                0.7,
-                (0, 255, 0),
-                2
-            )
-
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    return image
-
-
-# -------------------------
-# Gradio UI
-# -------------------------
-app = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="numpy", label="Upload Image"),
-    outputs=gr.Image(type="numpy", label="Detection Result"),
-    title="Buck Tracker AI – Deer Detection",
-    description="YOLO-based deer detection with automatic class labels from the model."
-)
-
-# -------------------------
-# Launch
-# -------------------------
-if __name__ == "__main__":
-    app.launch()
+mport gradio as gr
+import numpy as np
+from PIL import Image
+
+# -------------------------
+# Load detection model
+# -------------------------
+model = YOLO("buck_vs_doe_Detection_best.pt")
+
+# -------------------------
+# Inference function
+# -------------------------
+def predict(image):
+    # Run inference (YOLO accepts numpy RGB directly)
+    results = model(image)
+
+    # Take first result (single image)
+    r = results[0]
+
+    # Plot results (BGR numpy array)
+    im_bgr = r.plot()
+
+    # Convert BGR → RGB for Gradio
+    im_rgb = im_bgr[..., ::-1]
+
+    return im_rgb
+
+
+# -------------------------
+# Gradio UI
+# -------------------------
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.Image(type="numpy", label="Detection Result"),
+    title="Buck Tracker AI – Deer Detection",
+    description="YOLO-based buck vs doe detection using Ultralytics native plotting."
+)
+
+# -------------------------
+# Launch
+# -------------------------
+if __name__ == "__main__":
+    app.launch()