Update app1.py

app1.py

@@ -1,103 +1,21 @@
-import numpy as np
+# import tensorflow as tf
 import cv2
-import streamlit as st
-from tensorflow import keras
-from keras.models import model_from_json
-from keras.preprocessing.image import img_to_array
-from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
-
-# load model
-emotion_dict = {0:'angry', 1 :'happy', 2: 'neutral', 3:'sad', 4: 'surprise'}
-# load json and create model
-json_file = open('emotion_model1.json', 'r')
-loaded_model_json = json_file.read()
-json_file.close()
-classifier = model_from_json(loaded_model_json)
-
-# load weights into new model
-classifier.load_weights("emotion_model1.h5")
-
-#load face
-try:
-    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
-except Exception:
-    st.write("Error loading cascade classifiers")
-
-class VideoTransformer(VideoTransformerBase):
-    def transform(self, frame):
-        img = frame.to_ndarray(format="bgr24")
-
-        #image gray
-        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-        faces = face_cascade.detectMultiScale(
-            image=img_gray, scaleFactor=1.3, minNeighbors=5)
-        for (x, y, w, h) in faces:
-            cv2.rectangle(img=img, pt1=(x, y), pt2=(
-                x + w, y + h), color=(255, 0, 0), thickness=2)
-            roi_gray = img_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 = img_to_array(roi)
-                roi = np.expand_dims(roi, axis=0)
-                prediction = classifier.predict(roi)[0]
-                maxindex = int(np.argmax(prediction))
-                finalout = emotion_dict[maxindex]
-                output = str(finalout)
-            label_position = (x, y)
-            cv2.putText(img, output, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-
-        return img
-
-def main():
-    # Face Analysis Application #
-    st.title("Real Time Face Emotion Detection Application")
-    activiteis = ["Home", "Webcam Face Detection", "About"]
-    choice = st.sidebar.selectbox("Select Activity", activiteis)
-    st.sidebar.markdown(
-        
-    if choice == "Home":
-        html_temp_home1 = """<div style="background-color:#6D7B8D;padding:10px">
-                                            <h4 style="color:white;text-align:center;">
-                                            Face Emotion detection application using OpenCV, Custom CNN model and Streamlit.</h4>
-                                            </div>
-                                            </br>"""
-        st.markdown(html_temp_home1, unsafe_allow_html=True)
-        st.write("""
-                 The application has two functionalities.
-
-                 1. Real time face detection using web cam feed.
-
-                 2. Real time face emotion recognization.
-
-                 """)
-    elif choice == "Webcam Face Detection":
-        st.header("Webcam Live Feed")
-        st.write("Click on start to use webcam and detect your face emotion")
-        webrtc_streamer(key="example", video_transformer_factory=VideoTransformer)
-
-    elif choice == "By_Image":
-        st.subheader("About this app")
-        html_temp_about1= """<div style="background-color:#6D7B8D;padding:10px">
-                                    <h4 style="color:white;text-align:center;">
-                                    Real time face emotion detection application using OpenCV, Custom Trained CNN model and Streamlit.</h4>
-                                    </div>
-                                    </br>"""
-        st.markdown(html_temp_about1, unsafe_allow_html=True)
-
-        html_temp4 = """
-                             		<div style="background-color:#98AFC7;padding:10px">
-                             		
-                             		<h4 style="color:white;text-align:center;">Thanks for Visiting</h4>
-                             		</div>
-                             		<br></br>
-                             		<br></br>"""
-
-        st.markdown(html_temp4, unsafe_allow_html=True)
-
-    else:
-        pass
-
-
-if __name__ == "__main__":
-    main()
+import numpy as np
+# from glob import glob
+# from models import Yolov4
+import gradio as gr
+# model = Yolov4(weight_path="yolov4.weights", class_name_path='coco_classes.txt')
+def gradio_wrapper(img):
+    global model
+    #print(np.shape(img))
+    # results = model.predict(img)
+    # return results[0]
+demo = gr.Interface(
+    gradio_wrapper,
+    #gr.Image(source="webcam", streaming=True, flip=True),
+    gr.Image(source="webcam", streaming=True),
+    "image",
+    live=True
+)
+
+demo.launch()