Update app.py

app.py

@@ -9,9 +9,7 @@ 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 face, this person has a retention rate of 69%."
-
+def detect_and_write_on_faces(image, texts_to_write):
     # Convert the uploaded image to grayscale for face detection
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
@@ -22,23 +20,37 @@ def detect_and_write_on_faces(image):
     pil_image = PilImage.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
     draw = ImageDraw.Draw(pil_image)
 
-    for (x, y, w, h) in faces:
+    for i, (x, y, w, h) in enumerate(faces):
         # Write text on the detected face
-        draw.text((x, y - 10), text_to_write, fill=(255, 0, 0, 0))
+        if i < len(texts_to_write):
+            text_to_write = texts_to_write[i]
+            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 Emotion and Retention Detection")
+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)
+        
+        # Define different texts for each face
+        texts_to_write = [
+            "Happy face, this person has a retention rate of 69.",
+            "Smiling face, spreading positivity!",
+            "Serious face, focused and determined.",
+            "Surprised face, something caught their attention!",
+            "Confused face, deep in thought.",
+            "Excited face, full of energy!",
+            "Calm face, a picture of tranquility."
+        ]
+
+        result_image = detect_and_write_on_faces(input_image, texts_to_write)
 
         st.image(result_image, caption="Processed Image", use_column_width=True)