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app.py

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+# import the rquired libraries.
+import numpy as np
+import cv2
+from keras.models import load_model
+import streamlit as st
+from tensorflow import keras
+from tensorflow.keras.preprocessing.image import img_to_array
+from streamlit_webrtc import webrtc_streamer, VideoTransformerBase, RTCConfiguration, VideoProcessorBase, WebRtcMode
+
+emotion_labels = ['Angry','Disgust','Fear','Happy','Neutral', 'Sad', 'Surprise']
+
+classifier =load_model('model_78.h5')
+
+classifier.load_weights("model_weights_78.h5")
+
+try:
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+except Exception:
+    st.write("Error loading cascade classifiers")
+
+class VideoTransformer(VideoTransformerBase):
+    def transform(self, frame):
+        img = frame.to_ndarray(format="bgr24")
+
+        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        faces = face_cascade.detectMultiScale(
+            image=img_gray, scaleFactor=1.3, minNeighbors=5)
+        for (x, y, w, h) in faces:
+            cv2.rectangle(img=img, pt1=(x, y), pt2=(
+                x + w, y + h), color=(0, 255, 255), thickness=2)
+            roi_gray = img_gray[y:y + h, x:x + w]
+            roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
+            if np.sum([roi_gray]) != 0:
+                roi = roi_gray.astype('float') / 255.0
+                roi = img_to_array(roi)
+                roi = np.expand_dims(roi, axis=0)
+                prediction = classifier.predict(roi)[0]
+                maxindex = int(np.argmax(prediction))
+                finalout = emotion_labels[maxindex]
+                output = str(finalout)
+            label_position = (x, y-10)
+            cv2.putText(img, output, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+
+        return img
+
+def main():
+    # Face Analysis Application #
+    st.title("Real Time Face Emotion Detection Application 😠🤮😨😀😐😔😮")
+    activiteis = ["Home", "Live Face Emotion Detection"]
+    choice = st.sidebar.selectbox("Select Activity", activiteis)
+
+    # Homepage.
+    if choice == "Home":
+        html_temp_home1 = """<div style="background-color:#FC4C02;padding:0.5px">
+                             <h4 style="color:white;text-align:center;">
+                            Start Real Time Face Emotion Detection.
+                             </h4>
+                             </div>
+                             </br>"""
+
+        st.markdown(html_temp_home1, unsafe_allow_html=True)
+        st.write("""
+        How to use...
+        1. Click the dropdown list in the top left corner and select Live Face Emotion Detection.
+        2. This takes you to a page which will tell if it recognizes your emotions.
+                 """)
+
+    # Live Face Emotion Detection.
+    elif choice == "Live Face Emotion Detection":
+        st.header("Webcam Live Feed")
+        st.subheader('''
+        Welcome to the other side of the SCREEN!!!
+        * Get ready with all the emotions you can express. 
+        ''')
+        st.write("1. Click Start to open your camera and give permission for prediction")
+        st.write("2. This will predict your emotion.")
+        st.write("3. When you done, click stop to end.")
+        webrtc_streamer(key="example", video_processor_factory=VideoTransformer)
+
+    else:
+        pass
+
+
+if __name__ == "__main__":
+    main()

model_78.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ae8de71c7742aa40cf3a5b685d95e56070592a65b9e419bdd2b633a720c0e0f3
+size 32828272

model_weights_78.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0d45e7f0123004cbc60a1cd51e6860bd18c03a9d42238b507ee281a984ae51ce
+size 10966264

requirements.txt

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+numpy
+streamlit==1.9.0
+tensorflow-cpu==2.9.0
+opencv-python-headless==4.5.5.64
+streamlit-webrtc==0.37.0
+protobuf~=3.19.0
+pandas
+seaborn
+pyngrok