Added required files

.gitattributes

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README.md

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----
-title: Mice Paw Detector
-emoji: 💻
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 5.9.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: Mice Paw Detection for cylinder test
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Mice Paw Detector
+emoji: 🐾
+colorFrom: indigo
+colorTo: pink
+sdk: gradio
+sdk_version: 5.9.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: Mice Paw Detection for cylinder test
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import cv2
+from ultralytics import YOLO
+
+# Load YOLO model
+model = YOLO('last.torchscript')  # Replace with 'best.onnx' or 'best.torchscript' if converted
+
+# Function for image inference
+def detect_in_image(image):
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    results = model.predict(source=image, save=False, save_txt=False)
+    annotated_frame = results[0].plot()  # Annotated frame with bounding boxes
+    annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)  # Convert to RGB
+    return annotated_frame
+
+# Function for video inference
+def detect_in_video(video):
+    cap = cv2.VideoCapture(video)
+    output_path = "output_video.mp4"
+
+    # Get video properties
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    # Create VideoWriter for saving the output video
+    out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
+
+    # Frame generator for live streaming
+    def frame_generator(frame_skip=6):
+        frame_count = 0
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            if frame_count % frame_skip == 0:  # Process every nth frame
+                results = model.predict(source=frame, save=False, save_txt=False)
+                annotated_frame = results[0].plot()  # Annotated frame with bounding boxes
+                
+                # Save annotated frame to output video
+                out.write(annotated_frame)
+
+                # Convert frame to RGB for display
+                annotated_frame_rgb = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
+                yield annotated_frame_rgb
+            
+            frame_count += 1
+
+        # Release resources
+        cap.release()
+        out.release()
+
+    return frame_generator(), output_path
+
+# Build the Gradio interface
+with gr.Blocks(css=".header {font-size: 30px; color: #4CAF50; font-weight: bold; text-align: center;} .image-output {max-width: 400px; margin: auto;}") as app:
+    gr.Markdown("<h1 class='header'>🐾 Rat Paw Detection App 🐾</h1>")
+    
+    # Image detection tab
+    with gr.Tab("Image Detection"):
+        image_input = gr.Image(label="Upload an Image", type="numpy")
+        image_output = gr.Image(label="Annotated Image", type="numpy", elem_id="image-output")
+        image_button = gr.Button("Detect", variant="primary")
+        image_button.click(detect_in_image, inputs=image_input, outputs=image_output)
+    
+    # Video detection tab
+    with gr.Tab("Video Detection"):
+        video_input = gr.Video(label="Upload a Video")
+        video_display = gr.Image(label="Live Detection", elem_id="image-output")
+
+        def video_handler(video):
+            frame_gen, output_path = detect_in_video(video)
+            for frame in frame_gen:
+                yield {video_display: frame}  # Live update for each processed frame
+
+        video_button = gr.Button("Detect", variant="primary")
+        video_button.click(fn=video_handler, inputs=video_input, outputs=[video_display])
+
+# Launch the app
+app.launch()

best.pt

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