Create app.py

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from camera_input_live import camera_input_live
+
+# Set page config
+st.set_page_config(page_title="Real-Time Video AI Demo", layout="wide")
+
+# Title and description
+st.title("Real-Time Video AI Processing with Streamlit")
+st.write("This app demonstrates real-time webcam video processing with AI filters using Streamlit and OpenCV.")
+
+# Sidebar for filter selection
+st.sidebar.header("Filter Controls")
+filter_type = st.sidebar.selectbox(
+    "Choose a filter",
+    ["None", "Grayscale", "Canny Edge Detection", "Blur"]
+)
+
+# Initialize the camera input
+image = camera_input_live()
+
+# Process and display the video feed
+if image is not None:
+    # Convert the image from bytes to numpy array
+    nparr = np.frombuffer(image.getvalue(), np.uint8)
+    frame = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    
+    # Apply selected filter
+    if filter_type == "Grayscale":
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        # Convert back to 3 channels for Streamlit display
+        frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+    elif filter_type == "Canny Edge Detection":
+        frame = cv2.Canny(frame, 100, 200)
+        # Convert back to 3 channels for Streamlit display
+        frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
+    elif filter_type == "Blur":
+        frame = cv2.GaussianBlur(frame, (15, 15), 0)
+    
+    # Display the processed frame
+    st.image(frame, channels="RGB", caption="Live Video Feed")
+else:
+    st.warning("Waiting for camera input...")
+
+# Instructions
+st.sidebar.markdown("""
+### Instructions
+1. Allow camera access when prompted
+2. Select a filter from the dropdown
+3. View the processed video in real-time
+4. Try different filters to see AI processing in action
+""")
+
+# Requirements note
+st.sidebar.info("Make sure you have installed: streamlit, opencv-python, numpy, streamlit-camera-input-live")