Update app.py

app.py

@@ -1,7 +1,8 @@
 import streamlit as st
-import numpy as np
 import cv2
+import numpy as np
 from PIL import Image
+from fer import FER  
 
 # Set the page config
 st.set_page_config(page_title="Emotion Recognition App", layout="centered")
@@ -11,16 +12,8 @@ st.title("Emotion Recognition App")
 # Upload an image
 uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
-# Load OpenCV's pre-trained face detection model
-face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
-
-# Define a simple emotion detection function
-def detect_emotion(face):
-    """
-    Mock function to assign a random emotion.
-    Replace with an actual emotion detection model.
-    """
-    return "Happy"  # Replace with your logic
+# Load FER emotion detection model
+emotion_detector = FER(mtcnn=True)  # Use MTCNN for better face detection
 
 # Resize image to reduce memory usage
 def resize_image(image, max_size=(800, 800)):
@@ -43,24 +36,24 @@ if uploaded_file is not None:
         # Convert image to numpy array
         image_np = np.array(image)
 
-        # Convert image to grayscale for face detection
-        gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+        # Detect emotions
+        results = emotion_detector.detect_emotions(image_np)
 
-        # Detect faces
-        faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+        if results:
+            for face in results:
+                # Get bounding box and detected emotion
+                box = face["box"]
+                emotions = face["emotions"]
+                dominant_emotion = max(emotions, key=emotions.get)
 
-        if len(faces) > 0:
-            for (x, y, w, h) in faces:
-                # Draw rectangle around the face
+                # Draw a rectangle around the face
+                x, y, w, h = box
                 cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2)
 
-                # Assign a dummy emotion
-                emotion = detect_emotion(None)
-
-                # Display emotion above the face
+                # Display detected emotion
                 cv2.putText(
                     image_np,
-                    emotion,
+                    dominant_emotion,
                     (x, y - 10),
                     cv2.FONT_HERSHEY_SIMPLEX,
                     0.9,
@@ -71,4 +64,4 @@ if uploaded_file is not None:
             # Display the processed image
             st.image(image_np, caption="Processed Image", use_column_width=True)
         else:
-            st.warning("No faces detected in the image.")
+            st.warning("No faces detected or unable to determine emotions.")