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app.py

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+import cv2
+import streamlit as st
+from streamlit_webrtc import VideoTransformerBase, webrtc_streamer
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.applications.efficientnet_v2 import preprocess_input
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+
+# Load your emotion classification model
+def load_model(model_path, weights_path):
+    with open(model_path, 'r') as json_file:
+        model_json = json_file.read()
+    loaded_model = tf.keras.models.model_from_json(model_json)
+    loaded_model.load_weights(weights_path)
+    return loaded_model
+
+# Preprocessing function for the image
+def preprocess_image(img, target_size=(224, 224)):
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    img = img.resize(target_size)
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = preprocess_input(img_array)
+    return img_array
+
+# Load the model
+model = load_model('model.json', 'model_weights.h5')
+
+# Load the pre-trained face detection model
+faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+class_names = ['Ahegao', 'Angry', 'Happy', 'Neutral', 'Sad', 'Surprise']
+
+class VideoTransformer(VideoTransformerBase):
+    def __init__(self):
+        self.i = 0
+
+    def transform(self, frame):
+        img = frame.to_ndarray(format="bgr24")
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        faces = faceCascade.detectMultiScale(gray, 1.3, 5)
+        i = self.i + 1
+
+        for (x, y, w, h) in faces:
+            face_img = img[y:y + h, x:x + w]
+            face_img = cv2.cvtColor(face_img, cv2.COLOR_BGR2RGB)
+            pil_img = Image.fromarray(face_img)
+            preprocessed_img = preprocess_image(pil_img)
+            predictions = model.predict(preprocessed_img)
+            predicted_class = np.argmax(predictions, axis=1)[0]
+
+            cv2.rectangle(img, (x, y), (x + w, y + h), (95, 207, 30), 3)
+            cv2.rectangle(img, (x, y - 40), (x + w, y), (95, 207, 30), -1)
+            cv2.putText(img, class_names[predicted_class], (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2)
+
+        return img
+
+def main():
+    st.title("Facial Emotion Detection")
+    st.sidebar.title("Dashboard")
+    st.sidebar.markdown(
+        """ Developed by Uday Singh    
+            Model: EfficientnetV2  
+            LinkedIn: https://www.linkedin.com/in/uday-singh-45563a209/""")
+    option = st.sidebar.selectbox("Choose an option",
+                                  ["Home", "Real-time Facial Emotion Analysis", "Emotion Analysis for Local Images",
+                                   "About"])
+    if option == "Home":
+        html_temp_home1 = """<div style="background-color:#6D7B8D;padding:10px">
+                                            <h4 style="color:white;text-align:center;">
+                                            Face Emotion detection application using OpenCV, EfficientNetV2 and Streamlit.</h4>
+                                            </div>
+                                            </br>"""
+        st.markdown(html_temp_home1, unsafe_allow_html=True)
+        st.write("""
+                 The application has two functionalities.
+
+                 1. Real time face detection using web cam feed.
+
+                 2. Real time facial emotion recognition.
+                 
+                 3. Emotion Prediction for local Images
+
+                 """)
+
+    elif option == "Real-time Facial Emotion Analysis":
+        webrtc_streamer(key="example", video_transformer_factory=VideoTransformer)
+
+    elif option == "Emotion Analysis for Local Images":
+        st.write("Emotion Analysis for a Single Image")
+        single_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+        if single_image is not None:
+            img = Image.open(single_image)
+            preprocessed_img = preprocess_image(img)
+            predictions = model.predict(preprocessed_img)
+            predicted_class = np.argmax(predictions, axis=1)[0]
+            st.write("Predicted Emotion for Uploaded Image:", class_names[predicted_class])
+
+    elif option == "About":
+        st.subheader("About this app")
+        html_temp_about1 = """<div style="padding:10px">  Real time face emotion detection application using OpenCV, 
+        Custom Trained CNN model and Streamlit. <br><br>
+        This Application is developed by Uday Singh using Streamlit Framework, Opencv, Tensorflow and Keras library for demonstration purpose. 
+        If you're on LinkedIn and want to connect, just click on the link in sidebar and shoot me a request.<br><br>
+        Thanks for Visiting... </div> </br> """
+        st.markdown(html_temp_about1, unsafe_allow_html=True)
+
+
+if __name__ == "__main__":
+    main()

model_weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:241fdd3caecf80777c170c7f3cdf51283eeef53e745e9384b609955fd9c532e1
+size 214930856

requirements.txt

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+streamlit
+numpy
+tensorflow==2.13.0
+keras
+pillow
+opencv-python-headless
+streamlit-webrtc