added docstrings, used more consistent variable names

app.py

@@ -10,6 +10,14 @@ from PIL import Image
 
 #gradio interface
 def process_image(img):
+    """
+    Parameters:
+        img: np.ndarray
+            an image (e.g., returned by cv2.imread)
+    Returns:
+        img: np.ndarray
+             an image annotated with the bounding box and label
+    """
     cv2_face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 
                                              'haarcascade_frontalface_default.xml')
     img = resize_img(img)
@@ -21,30 +29,69 @@ def process_image(img):
     return(img)
 
 def annotate_objects_in_image(img, obj_cascade, labeldict, model):
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    objects = obj_cascade.detectMultiScale(gray, 1.1, 4)
-    model_result = ""
+    """
+    Parameters:
+        img: np.ndarray
+            an image (e.g., returned by cv2.imread)
+        obj_cascade: cv2.CascadeClassifier
+                     OpenCV cascade classifier that can detect certain objects
+        labeldict: dict
+                   a dictionary for decoding the model predictions, (e.g., {0:happy, 1:sad})
+        model: keras.engine.functional.Functional
+               a Keras model instance (e.g., that is returned by keras.models.load_model) 
+    Returns:
+        img: np.ndarray
+             an image annotated with the bounding box and label
+        emotion : str
+                  predicted emotion of the face image
+    """
+    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    objects = obj_cascade.detectMultiScale(img_gray, 1.1, 4)
+    emotion = ""
     try:
         for (x, y, w, h) in objects:
             face = img[y-50:y+h+50, x-50:x+w+50]
-            model_result = predict_emotion_from_image(face, labeldict, model)
+            emotion, prob = predict_emotion_from_image(face, labeldict, model)
             font = cv2.FONT_HERSHEY_SIMPLEX
-            cv2.putText(img, model_result, (x, y), font, 1, (0, 0, 255), 2, cv2.LINE_AA)
+            cv2.putText(img, emotion, (x, y), font, 1, (0, 0, 255), 2, cv2.LINE_AA)
             cv2.rectangle(img, (x-25, y-25), (x+w+25, y+h+25), (0, 255, 0), 2)
     except Exception as ex:
-        model_result = ""
-    return img, model_result
+        emotion = ""
+    return img, emotion
     
 def predict_emotion_from_image (face_raw, labeldict, model):
-    face = cv2.resize(face_raw, (48, 48))
-    face_array = np.array(face)/255
-    face_array_4dims = np.expand_dims(face_array, axis=0)
-    prediction_vec = model.predict(face_array_4dims)
+    """
+    Parameters:
+        face_raw: np.ndarray
+                  a square-like image of a human face (e.g., returned by cv2.imread)
+        label_dict: dict
+                    a dictionary for decoding the model predictions, (e.g., {0:happy, 1:sad})
+        model: keras.engine.functional.Functional
+               a Keras model instance (e.g., that is returned by keras.models.load_model)
+    Returns:
+        emotion: str
+                 predicted emotion of the face image
+        prob: float
+              percent probability of the predicted emotion
+    """
+    face_res_arr = np.array(cv2.resize(face_raw, (48, 48)))
+    face_res_arr_gray = face_res_arr/255
+    face_res_arr_gray_4dims = np.expand_dims(face_res_arr_gray, axis=0)
+    prediction_vec = model.predict(face_res_arr_gray_4dims)
     prediction = np.argmax(prediction_vec)
     emotion = labeldict[prediction]
-    return (emotion)
+    prob = prediction_vec.max()*100
+    return (emotion,prob)
 
 def resize_img(img):
+    """
+    Parameters:
+        img: np.ndarray
+            a potentially oversized image (e.g., returned by cv2.imread)
+    Returns:
+        img: np.ndarray
+             a resized and potentially L-R flipped image
+    """
     if img.shape[0]> 960:
         img_AR = img.shape[0]/img.shape[1]
         #print(f'img shape: {img.shape}; aspect ratio: {img_AR}')