Update app.py

app.py

@@ -1,13 +1,16 @@
 import cv2
 import streamlit as st
 from fer import FER
+from PIL import Image
 import numpy as np
 
-# Initialize Emotion Detector
+# Initialize emotion detector
 emotion_detector = FER()
 
-# Function to process each frame and detect emotions
-def process_frame(frame):
+# Function to process image and detect emotions
+def process_image(image):
+    frame = np.array(image)
+    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)  # Convert from PIL to OpenCV format
     emotions = emotion_detector.detect_emotions(frame)
     blurred_frame = cv2.GaussianBlur(frame, (51, 51), 0)
 
@@ -15,16 +18,13 @@ def process_frame(frame):
         (x, y, w, h) = face["box"]
         emotion, score = max(face["emotions"].items(), key=lambda item: item[1])
 
-        # Overlay rectangle on detected face
         overlay = frame.copy()
         alpha = 0.4
         cv2.rectangle(overlay, (x, y), (x + w, y + h), (0, 255, 0), 2)
         cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0, frame)
 
-        # Replace detected face region with unblurred version
         blurred_frame[y:y + h, x:x + w] = frame[y:y + h, x:x + w]
 
-        # Display Emotion Text
         font = cv2.FONT_HERSHEY_SIMPLEX
         font_scale = 0.7
         font_thickness = 2
@@ -39,38 +39,22 @@ def process_frame(frame):
         cv2.rectangle(blurred_frame, (text_x - 5, text_y - text_height - 5), (text_x + text_width + 5, text_y + 5), bg_color, -1)
         cv2.putText(blurred_frame, text, (text_x, text_y), font, font_scale, text_color, font_thickness)
 
-    return blurred_frame
+    return cv2.cvtColor(blurred_frame, cv2.COLOR_BGR2RGB)  # Convert back to RGB for Streamlit
 
 # Streamlit UI
-st.title("Live Emotion Recognition")
-st.write("Allow camera access to start real-time emotion detection.")
+st.title("Real-Time Emotion Recognition")
+st.write("Use the camera or upload an image to detect emotions.")
 
-# Button to start webcam
-run = st.checkbox("Start Camera")
+# Camera Input
+camera_image = st.camera_input("Take a picture")
 
-# Streamlit Image Containers
-col1, col2 = st.columns(2)
-with col1:
-    st.header("Live Camera Feed")
-    camera_placeholder = st.image([])
+# File Upload
+uploaded_file = st.file_uploader("Or upload an image...", type=["jpg", "png", "jpeg"])
 
-with col2:
-    st.header("Emotion Detection Output")
-    output_placeholder = st.image([])
+# Process image if uploaded or captured via camera
+if camera_image or uploaded_file:
+    image = Image.open(camera_image if camera_image else uploaded_file)
+    st.image(image, caption="Captured Image", use_column_width=True)
 
-# OpenCV Video Capture
-cap = cv2.VideoCapture(0)
-
-while run:
-    ret, frame = cap.read()
-    if not ret:
-        st.error("Error: Unable to access webcam.")
-        break
-
-    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert to RGB
-    processed_frame = process_frame(frame)  # Detect emotions
-
-    camera_placeholder.image(frame, channels="RGB")
-    output_placeholder.image(processed_frame, channels="RGB")
-
-cap.release()
+    processed_image = process_image(image)
+    st.image(processed_image, caption="Processed Image with Emotions", use_column_width=True)