attempt to use emotion detection model

app.py

@@ -1,13 +1,59 @@
 import gradio as gr
 
+import tensorflow as tf
+import cv2
+import numpy as np
+
+
 def greet(name):
 	return "Hello " + name + "!!"
 
 
+
+# Load the saved model
+model = tf.keras.models.load_model('model/cnn_9_layer_model.h5')
+
+# Define the face cascade and emotions
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
+
+# Define the predict_emotion function
+def predict_emotion(frame):
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+    for (x, y, w, h) in faces:
+        face = gray[y:y+h, x:x+w]
+        face = cv2.resize(face, (48, 48))
+        face = np.expand_dims(face, axis=-1)
+        face = np.expand_dims(face, axis=0)
+        prediction = model.predict(face)
+        emotion = emotions[np.argmax(prediction)]
+        cv2.putText(frame, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+    return frame
+
+# Start the video capture and emotion detection
+# cap = cv2.VideoCapture(0)
+# while True:
+#     ret, frame = cap.read()
+#     if ret:
+#         frame = predict_emotion(frame)
+#         cv2.imshow('Live Facial Emotion Detection', frame)
+#     if cv2.waitKey(1) == ord('q'):
+#         break
+# cap.release()
+# cv2.destroyAllWindows()
+
+
+image = gr.inputs.Image(source = "webcam", shape=(50,50) )
+label = gr.outputs.Label(num_top_classes=7)
+
+
 iface = gr.Interface(
-	fn = greet, 
-	inputs="text", 
-	outputs="text",
+	fn = predict_emotion, 
+	inputs=image, 
+	outputs=label,
+	interpretation = "default",
 	title = "Mood Detective",
 	description = "real-time emotion detection"
 	)