add initial app

README.md

@@ -1,6 +1,6 @@
 ---
 title: Community Fish Detector
-emoji: 😻
+emoji: 🐟
 colorFrom: blue
 colorTo: gray
 sdk: gradio
@@ -10,5 +10,3 @@ pinned: false
 license: agpl-3.0
 short_description: WildHackers Community Fish Detector (CFD)
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import torch
+from ultralytics import YOLO
+import os
+
+REPO_URL = "https://github.com/WildHackers/community-fish-detector"
+MODEL_URL = REPO_URL + "/releases/download/cfd-1.00-yolov12x/cfd-yolov12x-1.00.pt"
+
+# Download model once
+MODEL_PATH = os.path.basename(MODEL_URL)
+if not os.path.exists(MODEL_PATH):
+    torch.hub.download_url_to_file(MODEL_URL, MODEL_PATH)
+
+# Load YOLOv12x model
+model = YOLO(MODEL_PATH)
+
+def run_detection(input_image, conf_threshold=0.60, iou_threshold=0.45, imgsz=640):
+    """
+    Runs YOLOv12x inference on an image.
+    Returns annotated image result.
+    """
+    if input_image is None:
+        return None
+
+    results = model.predict(
+        source=input_image,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        imgsz=imgsz,
+        save=False,
+        verbose=False
+    )
+
+    return results[0].plot()
+
+# Gradio interface
+demo = gr.Interface(
+    fn=run_detection,
+    inputs=[
+        gr.Image(type="numpy", label="Input Image"),
+        gr.Slider(0, 1, value=0.60, step=0.01, label="Confidence Threshold"),
+        gr.Slider(0, 1, value=0.45, step=0.01, label="IoU Threshold"),
+        gr.Slider(320, 1280, value=640, step=32, label="Image Size"),
+    ],
+    outputs=gr.Image(type="numpy", label="Detected Output"),
+    title="Community Fish Detector (YOLOv12x)",
+    description=(
+        f"Upload an image or video to detect fish using the [Community Fish Detector]({REPO_URL})."
+    ),
+    flagging_mode="never",
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+ultralytics>=8.3.0
+torch