Update app.py

app.py

@@ -8,38 +8,72 @@ import csv
 from datetime import datetime
 
 ############################################
-import matplotlib.pyplot as plt
-import pylab # this allows you to control figure size 
-pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook
-
-import io
-import streamlit as st
-bytes_data=None
-
+# import matplotlib.pyplot as plt
+# import pylab # this allows you to control figure size 
+# pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook
+
+# import io
+# import streamlit as st
+# bytes_data=None
+from flask_socketio import SocketIO,emit
+import base64
 ##################################################3
 
-import gradio as gr
+# import gradio as gr
 
 
 
 
 app = Flask(__name__)
-
-flag1 = True
-
-@app.route('/at')
-def testme():
-    global flag1
-    # return "i am in testme"
-    while flag1 is True:
+app.config['SECRET_KEY'] = 'secret!'
+socket = SocketIO(app,async_mode="eventlet")
+
+def base64_to_image(base64_string):
+    # Extract the base64 encoded binary data from the input string
+    base64_data = base64_string.split(",")[1]
+    # Decode the base64 data to bytes
+    image_bytes = base64.b64decode(base64_data)
+    # Convert the bytes to numpy array
+    image_array = np.frombuffer(image_bytes, dtype=np.uint8)
+    # Decode the numpy array as an image using OpenCV
+    image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+    return image
+
+@socket.on("connect")
+def test_connect():
+    print("Connected")
+    emit("my response", {"data": "Connected"})
+
+@socket.on("image")
+def receive_image(image):
+    # Decode the base64-encoded image data
+    image = base64_to_image(image)
+    image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
+    # emit("processed_image", image)
+    # Make the image a numpy array and reshape it to the models input shape.
+    image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)
+    image = (image / 127.5) - 1
+    # Predicts the model
+    prediction = model.predict(image)
+    index = np.argmax(prediction)
+    class_name = class_names[index]
+    confidence_score = prediction[0][index]
+    emit("result",{"name":str(class_name),"score":str(confidence_score)})
+# flag1 = True
+
+# @app.route('/at')
+# def testme():
+#     global flag1
+#     # return "i am in testme"
+#     while flag1 is True:
         
-        img_file_buffer=st.camera_input("Take a picture")
-        if img_file_buffer is not None:
-            test_image = Image.open(img_file_buffer)
-            st.image(test_image, use_column_width=True)
-        if bytes_data is None:
-            flag1 = False
-            st.stop()
+#         img_file_buffer=st.camera_input("Take a picture")
+#         if img_file_buffer is not None:
+#             test_image = Image.open(img_file_buffer)
+#             st.image(test_image, use_column_width=True)
+#         if bytes_data is None:
+#             flag1 = False
+#             st.stop()
 
 # def attend():
 #     # Face recognition variables