Create app.py

app.py

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+import streamlit as st
+import cv2
+
+# Load the pre-trained object detection model
+model = cv2.CascadeClassifier('path/to/haarcascade.xml')
+
+# Function to perform object recognition
+def detect_objects(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    objects = model.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+    for (x, y, w, h) in objects:
+        cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
+    return image
+
+# Streamlit app
+st.title('Object Recognition')
+uploaded_file = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png'])
+
+if uploaded_file is not None:
+    # Read the uploaded image
+    image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
+
+    # Perform object recognition
+    result = detect_objects(image)
+
+    # Display the result
+    st.image(result, channels='BGR')