Update app.py

app.py

@@ -1,25 +1,32 @@
-import os
-import cv2
-import numpy as np
+from keras.models import load_model
 from PIL import Image
-import face_recognition
-import streamlit as st
+import numpy as np
+import cv2 
 import requests
+import face_recognition
+import os
+from datetime import datetime
 
-# Set up Streamlit
-st.title("Face Recognition App")
+#the following are to do with this interactive notebook code
+from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
+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
 
-# Load images from the current directory
 Images = []
 classnames = []
 myList = os.listdir()
+#st.write(myList)
 for cls in myList:
-    if os.path.splitext(cls)[1] == ".jpg":
+    if os.path.splitext(cls)[1] == ".jpg"  :
         curImg = cv2.imread(f'{cls}')
         Images.append(curImg)
         classnames.append(os.path.splitext(cls)[0])
+st.write(classnames)
 
-# Function to find face encodings
 def findEncodings(Images):
     encodeList = []
     for img in Images:
@@ -31,47 +38,49 @@ def findEncodings(Images):
 encodeListknown = findEncodings(Images)
 st.write('Encoding Complete')
 
-# Take a picture using Streamlit camera input
-img_file_buffer = st.camera_input("Take a picture")
-
-# Check if an image was taken
+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)
-
-    # Convert the image to numpy array
-    image = np.asarray(test_image)
-
-    # Resize image
-    imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)
-    imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
-
-    # Find face locations and encodings
-    facesCurFrame = face_recognition.face_locations(imgS)
-    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
-
-    for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
-        matches = face_recognition.compare_faces(encodeListknown, encodeFace)
-        faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
-        matchIndex = np.argmin(faceDis)
-
-        if matches[matchIndex]:
-            name = classnames[matchIndex].upper()
-            st.write(name)
-            y1, x2, y2, x1 = faceLoc
-            y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
-            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            cv2.rectangle(image, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
-            cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+     
+     test_image = Image.open(img_file_buffer)
+     st.image(test_image, use_column_width=True)
+     
+     image = np.asarray(test_image)
+    
+     #########################
+     imgS = cv2.resize(image,(0,0),None,0.25,0.25)
+     imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
+     facesCurFrame = face_recognition.face_locations(imgS)
+     encodesCurFrame = face_recognition.face_encodings(imgS,facesCurFrame)
 
-            # Update data using requests
-            url = "https://rgiattendance.000webhostapp.com/update.php"
-            data1 = {'name': name}
-            response = requests.post(url, data=data1)
+     for encodeFace,faceLoc in zip(encodesCurFrame,facesCurFrame):
+         matches = face_recognition.compare_faces(encodeListknown,encodeFace)
+         faceDis = face_recognition.face_distance(encodeListknown,encodeFace)
+         #print(faceDis)
+         matchIndex = np.argmin(faceDis)
 
-            if response.status_code == 200:
-                st.write("Data updated on: " + url)
-            else:
-                st.write("Data NOT updated " + url)
+         if matches[matchIndex]:
+             name = classnames[matchIndex]
+             st.write(name)
+             y1, x2, y2, x1 = faceLoc
+             y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4
+             cv2.rectangle(image,(x1,y1),(x2,y2),(0,255,0),2)
+             cv2.rectangle(image,(x1,y2-35),(x2,y2),(0,255,0),cv2.FILLED)
+             cv2.putText(image,name,(x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX,1,(255, 255, 255),2)
 
-    st.image(image)
+             ##############
+             url = "https://kiwi-whispering-plier.glitch.me/update" 
+             
+             data = {
+                     'name': name,
+             }
+             response = requests.get(url, params=data)
+             
+             if response.status_code == 200 : 
+                 st.write(" data updated on : https://kiwi-whispering-plier.glitch.me" )
+             else : st.write("data not updated ")
+                 
+            ##############################
+                 
+     st.image(image)
+if bytes_data is None:
+     st.stop()