Update app.py

app.py

@@ -23,6 +23,7 @@ tokenizer = AutoTokenizer.from_pretrained("borisn70/bert-43-multilabel-emotion-d
 pipe = pipeline(task="text-classification", model=emotionDetectModel, tokenizer=tokenizer)
 
 face_emotion_detector = FER()
+cap = cv2.VideoCapture(0)
 
 localFormat = "%Y-%m-%d %H:%M:%S" #this is how will print the timestamp: year-month-day hour-minutes-seconds (army time)
 #currTime = datetime.now().astimezone().strftime(localFormat) this returns the time in the localFormat
@@ -34,6 +35,9 @@ def emotionAnalysis(message, face):
     Main function that processes both text and facial emotions
     Args:
         message (str): User input text
+        face: Image input from Gradio interface, can be either:
+            - numpy.ndarray: Direct webcam capture (RGB or BGR format)
+            - str: File path to uploaded image
     Returns:
         tuple: (str, plt) Contains the emotion results text and the updated plot
     """
@@ -52,14 +56,7 @@ def emotionAnalysis(message, face):
     text_dataDict["Confidence Score"].append(round(text_score, 2))
     
     # Capture and process facial emotion
-    # Load the image using OpenCV (BGR format)
-    img = cv2.imread(face)
-    # Convert the image to RGB (FER uses RGB, OpenCV loads images in BGR format)
-    if img is None:
-        print("Error: Could not load the image. Check the image path.")
-        return
-    
-    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img_rgb = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
 
     face_emotion, face_score = face_emotion_detector.top_emotion(img_rgb)
     face_timestamp = datetime.now().astimezone().strftime(localFormat)
@@ -110,7 +107,7 @@ interface = gr.Interface(
             label="Enter your text",
             placeholder="Type your message here. Type 'quit' to see final results."
         ),
-        gr.Image(label="Webcam Facial Expression", source="webcam")
+        gr.Image(label="Webcam Facial Expression", sources=['webcam'])
     ],
     outputs=[
         gr.Text(label="Emotion Results"),