Update app.py

app.py

@@ -1,5 +1,4 @@
 import streamlit as st
-import face_recognition
 import numpy as np
 import cv2
 from PIL import Image
@@ -12,16 +11,16 @@ st.title("Emotion Recognition App")
 # Upload an image
 uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
 
-# Define simple emotion mapping based on facial features (for demonstration purposes)
-def detect_emotion(face_landmarks):
+# 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):
     """
-    A simple mock-up function for detecting emotions based on landmarks.
-    Replace with a more sophisticated model as needed.
+    Mock function to assign a random emotion.
+    Replace with an actual emotion detection model.
     """
-    if face_landmarks:
-        # Example: Assign "Happy" if eyes are close together
-        return "Happy"
-    return "Neutral"
+    return "Happy"  # Replace with your logic
 
 # Resize image to reduce memory usage
 def resize_image(image, max_size=(800, 800)):
@@ -44,30 +43,25 @@ if uploaded_file is not None:
         # Convert image to numpy array
         image_np = np.array(image)
 
-        # Convert image to RGB (ensure compatibility with face_recognition)
-        if len(image_np.shape) == 3 and image_np.shape[2] == 4:  # RGBA to RGB
-            image_np = cv2.cvtColor(image_np, cv2.COLOR_RGBA2RGB)
-        elif len(image_np.shape) == 3 and image_np.shape[2] == 3:  # BGR to RGB
-            image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
+        # Convert image to grayscale for face detection
+        gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
 
-        # Detect faces and landmarks
-        face_locations = face_recognition.face_locations(image_np)
-        face_landmarks_list = face_recognition.face_landmarks(image_np)
+        # Detect faces
+        faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
 
-        if face_locations:
-            for face_location, face_landmarks in zip(face_locations, face_landmarks_list):
-                # Draw a rectangle around the face
-                top, right, bottom, left = face_location
-                cv2.rectangle(image_np, (left, top), (right, bottom), (0, 255, 0), 2)
+        if len(faces) > 0:
+            for (x, y, w, h) in faces:
+                # Draw rectangle around the face
+                cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2)
 
-                # Detect emotion based on landmarks
-                emotion = detect_emotion(face_landmarks)
+                # Assign a dummy emotion
+                emotion = detect_emotion(None)
 
                 # Display emotion above the face
                 cv2.putText(
                     image_np,
                     emotion,
-                    (left, top - 10),
+                    (x, y - 10),
                     cv2.FONT_HERSHEY_SIMPLEX,
                     0.9,
                     (255, 0, 0),