Update app.py

app.py

@@ -1,38 +1,60 @@
 import streamlit as st
-from deepface import DeepFace
-from PIL import Image
+import face_recognition
 import numpy as np
+import cv2
+from PIL import Image
 
-# Application Title
-st.title("Emotion Recognition for Autism Support")
-
-# Upload Image Section
-uploaded_image = st.file_uploader("Upload an Image with a Face", type=["jpg", "jpeg", "png"])
-
-if uploaded_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
-    img_np = np.array(image)
-
-    try:
-        # Perform Emotion Analysis
-        st.write("Analyzing emotions...")
-        result = DeepFace.analyze(img_path=img_np, actions=["emotion"], enforce_detection=True)
-
-        # Extract and Display Emotions
-        if result and "emotion" in result:
-            emotions = result["emotion"]
-            st.write("Detected Emotions:")
-            st.json(emotions)
-
-            # 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}")
+# Set the page config
+st.set_page_config(page_title="Emotion Recognition App", layout="centered")
+
+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):
+    """
+    A simple mock-up function for detecting emotions based on landmarks.
+    Replace with a more sophisticated model as needed.
+    """
+    # Example: Arbitrarily assign "Happy" if eyes are close together
+    if face_landmarks:
+        return "Happy"
+    return "Neutral"
+
+# Process the uploaded image
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    image_np = np.array(image)
+
+    # Convert image to RGB
+    rgb_image = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
+
+    # Detect faces in the image
+    face_locations = face_recognition.face_locations(rgb_image)
+    face_landmarks_list = face_recognition.face_landmarks(rgb_image)
+
+    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)
+
+            # Detect emotion based on landmarks
+            emotion = detect_emotion(face_landmarks)
+
+            # Display emotion above the face
+            cv2.putText(
+                image_np,
+                emotion,
+                (left, top - 10),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                0.9,
+                (255, 0, 0),
+                2,
+            )
+
+        st.image(image_np, caption="Processed Image", use_column_width=True)
+    else:
+        st.warning("No faces detected in the image.")