Create app.py

code/app.py

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+
+import streamlit as st
+import cv2
+from keras.models import load_model  # Assuming you have Keras installed
+import numpy as np
+import webbrowser
+import requests
+import re
+import os
+import time
+
+
+#final_emotion = None
+# Function to play the first song from youtube queries
+def play_first_song(final_emotion):
+    search_query = f"https://www.youtube.com/results?search_query={final_emotion}+background+tunes"
+    #webbrowser.open(search_query)
+    #search_query = f"https://www.youtube.com/results?search_query=Hindi+sad+songs"
+    response = requests.get(search_query)
+    html_content = response.text
+    match = re.search(r'/watch\?v=([^\"]+)', html_content)
+    if match:
+        video_id = match.group(1)
+        video_url = f"https://www.youtube.com/watch?v={video_id}"
+        webbrowser.open(video_url)
+
+# Define the list of emotions (modify if your model uses different classes)
+emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
+model = load_model("model/fer2013_mini_XCEPTION.102-0.66.hdf5")
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+def predict_emotion():
+    FRAME_WINDOW = st.image([])
+    camera = cv2.VideoCapture(0)
+    emotion_label = None
+    placeholder = st.empty()
+    #run1 = st.checkbox("Play Song", key ="2")
+
+    while True:
+        ret, frame = camera.read()
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        # Detect faces in the frame
+        faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+
+        for (x, y, w, h) in faces:
+            # Extract the region of interest (ROI) which is the face
+            roi_gray = gray[y:y + h, x:x + w]
+            roi_gray = cv2.resize(roi_gray, (64, 64), interpolation=cv2.INTER_AREA)
+
+            # Normalize the pixel values
+            roi = roi_gray / 255.0
+
+            # Reshape the image for the model
+            roi = np.reshape(roi, (1, 64, 64, 1))
+
+            # Make a prediction using the pre-trained model
+            prediction = model.predict(roi)
+            emotion_label = emotions[np.argmax(prediction)]
+            # Draw a rectangle around the face and display the predicted emotion
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
+            cv2.putText(frame, emotion_label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
+            button_text = "Capture Emotion"
+            cv2.putText(frame, button_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+        FRAME_WINDOW.image(frame)
+
+
+def predict_emotion_button():
+    camera = cv2.VideoCapture(0)
+    ret, frame = camera.read()
+    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
+    for (x, y, w, h) in faces:
+        roi_gray = gray[y:y + h, x:x + w]
+        roi_gray = cv2.resize(roi_gray, (64, 64), interpolation=cv2.INTER_AREA)
+        roi = roi_gray / 255.0
+        roi = np.reshape(roi, (1, 64, 64, 1))
+        prediction = model.predict(roi)
+        emotion_label = emotions[np.argmax(prediction)]
+        play_first_song(emotion_label)
+        st.write("Song Detected is:", emotion_label)
+        print("Song Detected is:", emotion_label)
+
+
+def main():
+  """
+  The main function of the Streamlit app.
+  """
+
+  st.title("Facial Emotion Recognition App")
+  st.write("This app detects your facial expression and displays the predicted emotion.")
+  if st.button("Play Song"):
+    predict_emotion_button()
+  run = st.checkbox("Start Webcam", key ="1")
+  camera = cv2.VideoCapture(0)
+  #frame_width = st.slider("Camera Width", min_value=300, max_value=1000)
+  FRAME_WINDOW = st.image([])
+
+  while run:
+    predict_emotion()
+    time.sleep(20)
+  else:
+    camera.release()
+
+if __name__ == '__main__':
+  main()