Update app.py

app.py

@@ -18,6 +18,58 @@ emotion_classifier = load_model(emotion_model_path, compile=False)
 EMOTIONS = ['neutral','happiness','surprise','sadness','anger','disgust','fear','contempt','unknown']
 
 
+# def predict(frame):
+
+#     frame = imutils.resize(frame, width=300)
+#     gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+#     faces = face_detection.detectMultiScale(gray, scaleFactor=1.1,
+#                                             minNeighbors=5, minSize=(30, 30),
+#                                             flags=cv2.CASCADE_SCALE_IMAGE)
+
+#     frameClone = frame.copy()
+#     if len(faces) > 0:
+#         faces = sorted(faces, reverse=True,
+#                        key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]
+#         (fX, fY, fW, fH) = faces
+#         # Extract the ROI of the face from the grayscale image, resize it to a fixed 28x28 pixels, and then prepare
+#         # the ROI for classification via the CNN
+#         roi = gray[fY:fY + fH, fX:fX + fW]
+#         roi = cv2.resize(roi, (48, 48))
+#         roi = roi.astype("float") / 255.0
+#         roi = img_to_array(roi)
+#         roi = np.expand_dims(roi, axis=0)
+
+#         preds = emotion_classifier.predict(roi)[0]
+#         label = EMOTIONS[preds.argmax()]
+#     else:
+#         return frameClone, "Can't find your face"
+
+#     probs = {}
+#     cv2.putText(frameClone, label, (fX, fY - 10),
+#                 cv2.FONT_HERSHEY_DUPLEX, 1, (238, 164, 64), 1)
+#     cv2.rectangle(frameClone, (fX, fY), (fX + fW, fY + fH),
+#                   (238, 164, 64), 2)
+
+#     for (i, (emotion, prob)) in enumerate(zip(EMOTIONS, preds)):
+#         probs[emotion] = float(prob)
+
+#     return frameClone, probs
+
+# inp = gr.components.Image(sources="webcam", label="Your face")
+# out = [
+#     gr.components.Image(label="Predicted Emotion"),
+#     gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
+# ]
+# title = "Facial Emotion Recognition"
+# description = "How well can this model predict your emotions? Take a picture with your webcam, and it will guess if" \
+#               " you are: happy, sad, angry, disgusted, scared, surprised, or neutral."
+# thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-emotion-recognition/master/thumbnail.png"
+
+# # gr.Interface(predict, inp, out, capture_session=True, title=title, thumbnail=thumbnail,
+# #              description=description).launch(inbrowser=True)
+# gr.Interface(fn=predict, inputs=inp, outputs=out, title=title, thumbnail=thumbnail).launch()
+
+######################################################################################################################################################
 def predict(frame):
 
     frame = imutils.resize(frame, width=300)
@@ -55,16 +107,12 @@ def predict(frame):
 
     return frameClone, probs
 
-inp = gr.components.Image(sources="webcam", label="Your face")
+inp = gr.components.Video(sources="webcam", label="Your face")
 out = [
-    gr.components.Image(label="Predicted Emotion"),
+    gr.components.Video(label="Predicted Emotion"),
     gr.components.Label(num_top_classes=2, label="Top 2 Probabilities")
 ]
-title = "Facial Emotion Recognition"
-description = "How well can this model predict your emotions? Take a picture with your webcam, and it will guess if" \
-              " you are: happy, sad, angry, disgusted, scared, surprised, or neutral."
-thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-emotion-recognition/master/thumbnail.png"
 
-# gr.Interface(predict, inp, out, capture_session=True, title=title, thumbnail=thumbnail,
-#              description=description).launch(inbrowser=True)
 gr.Interface(fn=predict, inputs=inp, outputs=out, title=title, thumbnail=thumbnail).launch()
+    
+