fix

.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml

.idea/Mood Meter.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv/python_stuff" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.8" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

.idea/inspectionProfiles/Project_Default.xml

+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+          <option value="N801" />
+          <option value="N806" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>
\ No newline at end of file

.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Mood Meter.iml" filepath="$PROJECT_DIR$/.idea/Mood Meter.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

main.py

+#model.h5 is best model
+import cv2
+import warnings
+warnings.filterwarnings("ignore")
+from keras.models import  load_model
+import numpy as np
+import time
+
+class Main:
+    def main(self):
+        face_classifier = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+        classifier = load_model(r'model.h5')
+
+        # Angepasste emotion_labels Liste ohne 'Angry', 'Disgust', 'Fear'
+        emotion_labels = ['Happy', 'Neutral', 'Sad', 'Surprise']
+
+        cap = cv2.VideoCapture(0)
+
+        start_time = time.time()
+        emotion_history = []
+
+        while True:
+            _, frame = cap.read()
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = face_classifier.detectMultiScale(gray)
+
+            for (x, y, w, h) in faces:
+                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 255), 2)
+                roi_gray = 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 = np.expand_dims(roi, axis=0)
+
+                    prediction = classifier.predict(roi)[0]
+                    adjusted_prediction = np.delete(prediction, [0, 1, 2])
+                    max_index = np.argmax(adjusted_prediction)
+                    current_emotion = emotion_labels[max_index]
+                    label_position = (x, y)
+                    cv2.putText(frame, current_emotion, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+
+                    emotion_history.append(current_emotion)
+
+                    # Überprüfen, ob 3 Sekunden vergangen sind diese wird dann für die chat gpt api genutzt
+                    if time.time() - start_time >= 3:
+                        if emotion_history.count(current_emotion) == len(emotion_history):
+                            print(f"Konstante Emotion für 3 Sekunden: {current_emotion}")
+
+                        start_time = time.time()
+                        emotion_history.clear()
+
+            cv2.imshow('Emotion Detector', frame)
+            if cv2.waitKey(1) & 0xFF == ord('q'):
+                break
+
+        cap.release()
+        cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    main_instance = Main()
+    main_instance.main()
+
+