Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
-from fer import FER
-import cv2
+from deepface import DeepFace
 from PIL import Image
 import numpy as np
 
@@ -11,30 +10,29 @@ st.title("Emotion Recognition for Autism Support")
 uploaded_image = st.file_uploader("Upload an Image with a Face", type=["jpg", "jpeg", "png"])
 
 if uploaded_image:
-    # Load the image
+    # Display the uploaded image
     image = Image.open(uploaded_image)
     st.image(image, caption="Uploaded Image", use_column_width=True)
 
-    # Convert PIL Image to NumPy Array
+    # Convert PIL Image to NumPy array
     img_np = np.array(image)
 
-    # Convert RGB to BGR (required by OpenCV)
-    img_bgr = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+    try:
+        # Perform Emotion Analysis
+        st.write("Analyzing emotions...")
+        result = DeepFace.analyze(img_path=img_np, actions=["emotion"], enforce_detection=True)
 
-    # Initialize FER detector
-    detector = FER(mtcnn=False)
-
-    # Detect emotions
-    st.write("Analyzing emotions...")
-    result = detector.detect_emotions(img_bgr)
-
-    if result:
-        for face in result:
-            emotions = face["emotions"]
+        # Extract and Display Emotions
+        if result and "emotion" in result:
+            emotions = result["emotion"]
             st.write("Detected Emotions:")
             st.json(emotions)
-    else:
-        st.warning("No faces detected in the image. Please try another image.")
 
-    # Provide Friendly Feedback
-    st.write("Emotion recognition will be refined in future updates!")
+            # Provide Friendly Feedback
+            dominant_emotion = max(emotions, key=emotions.get)
+            st.success(f"The dominant emotion is: {dominant_emotion}")
+        else:
+            st.warning("No emotions detected. Please try with another image.")
+
+    except Exception as e:
+        st.error(f"An error occurred: {e}")