Create app.py

app.py

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+import gradio as gr
+
+from keras.preprocessing.image import img_to_array
+import imutils
+import cv2
+from keras.models import load_model
+import numpy as np
+
+# parameters for loading data and images
+detection_model_path = 'haarcascade_frontalface_default.xml'
+emotion_model_path = '_mini_XCEPTION.102-0.66.hdf5'
+
+# hyper-parameters for bounding boxes shape
+# loading models
+face_detection = cv2.CascadeClassifier(detection_model_path)
+emotion_classifier = load_model(emotion_model_path, compile=False)
+EMOTIONS = ["angry", "disgusted", "scared", "happy", "sad", "surprised",
+            "neutral"]
+
+
+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, (64, 64))
+        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.inputs.Image(source="webcam", label="Your face")
+out = [
+    gr.outputs.Image(label="Predicted Emotion"),
+    gr.outputs.Label(num_top_classes=3, label="Top 3 Probabilities")
+]
+title = "Emotion Classification"
+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)