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app.py

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+import streamlit as st
+import cv2
+import matplotlib.pyplot as plt
+from inference import Stream
+import supervision as sv
+
+annotator = sv.BoxAnnotator()
+
+def main():
+    st.title("RTSP Stream with Hugging Face Streamlit")
+
+    # Set up camera connection details
+    ip_address = '65.108.95.124'  # IP address of the camera
+    port = '554'  # Port number for the RTSP stream
+    username = ''  # Username (if required by the camera)
+    password = ''  # Password (if required by the camera)
+
+    # Construct the RTSP stream URL
+    rtsp_url = f"rtsp://{username}:{password}@{ip_address}:{port}/testia/live"
+
+    # Display the RTSP stream using Hugging Face Streamlit
+    stframe = st.empty()
+    predictions_frame = st.empty()
+
+    with Stream(
+        source=rtsp_url,
+        model="jakojdnfin/1",
+        output_channel_order="BGR",
+        use_main_thread=True,  # for OpenCV display
+        on_prediction=lambda predictions, image: update_predictions(predictions, image, predictions_frame)
+    ) as stream:
+        while True:
+            image = stream.read()
+            if image is not None and not image.empty():  # Check if the image is not empty
+                # Display the image using matplotlib in the Streamlit app
+                fig, ax = plt.subplots()
+                ax.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+                ax.axis('off')  # Hide axis
+                stframe.pyplot(fig)
+
+def update_predictions(predictions, image, predictions_frame):
+    # Update predictions frame with annotated image
+    annotated_image = annotator.annotate(scene=image, detections=sv.Detections.from_roboflow(predictions))
+    predictions_frame.image(cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB), use_column_width=True)
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+inference
+supervision
+opencv-python