Create app.py

app.py

@@ -0,0 +1,51 @@
+import streamlit as st
+import cv2
+from PIL import Image as PilImage
+from PIL import ImageDraw
+import numpy as np
+import io
+import base64
+
+# Load the face detection classifier
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+def detect_and_write_on_faces(image):
+    text_to_write = "Happy faces, this person has a retention rate of 69% in a balanced way."
+
+    # Convert the uploaded image to grayscale for face detection
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the image
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    # Convert the OpenCV image to a Pillow image
+    pil_image = PilImage.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
+    draw = ImageDraw.Draw(pil_image)
+
+    for (x, y, w, h) in faces:
+        # Write text on the detected face
+        draw.text((x, y - 10), text_to_write, fill=(255, 0, 0, 0))
+
+    # Convert the Pillow image back to OpenCV format
+    image_with_text = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+
+    return image_with_text
+
+st.title("Face Detection and Text Writing")
+
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
+
+if uploaded_image is not None:
+    if st.button("Process Image"):
+        input_image = cv2.imdecode(np.frombuffer(uploaded_image.read(), np.uint8), -1)
+        result_image = detect_and_write_on_faces(input_image)
+
+        st.image(result_image, caption="Processed Image", use_column_width=True)
+
+        # Allow the user to download the processed image as a JPEG file
+        output_buffer = io.BytesIO()
+        PilImage.fromarray(result_image).save(output_buffer, format="JPEG")
+        st.markdown("### Download Processed Image")
+        st.markdown(
+            f"Download your processed image [here](data:file/jpeg;base64,{base64.b64encode(output_buffer.getvalue()).decode()})"
+        )