Update app.py

app.py

@@ -1,44 +1,40 @@
 import streamlit as st
-from PIL import Image, ImageOps
+from PIL import Image
 import numpy as np
 import requests
+from io import BytesIO
+import cv2
+from fer import FER  # Ensure this package is in requirements.txt
 
-# Function for image preprocessing
-def preprocess_image(image):
-    # Convert image to grayscale
-    gray_image = ImageOps.grayscale(image)
-    # Resize the image to 48x48 (common for emotion recognition models)
-    resized_image = gray_image.resize((48, 48))
-    # Convert the image to a numpy array
-    image_array = np.array(resized_image)
-    # Normalize the image array (values between 0 and 1)
-    normalized_image = image_array / 255.0
-    return normalized_image
+# Title and Description
+st.title("Emotion Recognition App")
+st.write("Upload a face image to detect emotions using AI!")
 
-# Title and description
-st.title("Emotion Recognition for Autism Support")
-st.write("Upload an image, and the app will help identify emotions.")
+# File upload section
+uploaded_file = st.file_uploader("Upload a face image", type=["jpg", "jpeg", "png"])
 
-# Upload image section
-uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-if uploaded_file:
-    # Load the image using PIL
+if uploaded_file is not None:
+    # Display the uploaded image
     image = Image.open(uploaded_file)
     st.image(image, caption="Uploaded Image", use_column_width=True)
-    st.success("Image uploaded successfully!")
-    
-    # Preprocess the image
-    st.write("Preprocessing the image for emotion recognition...")
-    preprocessed_image = preprocess_image(image)
-    st.write("Preprocessing complete. Ready for emotion analysis.")
 
-    # Display preprocessed image
-    st.image(Image.fromarray((preprocessed_image * 255).astype('uint8')), caption="Preprocessed Image")
+    # Convert image to a numpy array
+    img_array = np.array(image)
+
+    # Process the image for emotion detection
+    st.write("Detecting emotions...")
+    detector = FER(mtcnn=True)  # Initialize the FER+ detector
 
-    # Placeholder for emotion recognition (to be integrated with a model later)
-    st.info("Emotion recognition will be added in the next step.")
+    try:
+        # Detect emotions
+        result = detector.top_emotion(img_array)
+        if result:
+            emotion, score = result
+            st.write(f"Detected Emotion: **{emotion.capitalize()}** with confidence {score * 100:.2f}%")
+        else:
+            st.write("No clear emotion detected. Please try another image.")
+    except Exception as e:
+        st.error(f"Error processing the image: {e}")
 
 # Footer
-st.write("---")
-st.write("Developed to assist children with autism in recognizing emotions.")
+st.write("Emotion recognition powered by FER+")