Update app.py

app.py

@@ -1,34 +1,25 @@
-from keras.models import load_model
-from PIL import Image
+import os
+import cv2
 import numpy as np
-import cv2 
-import requests
-
+from PIL import Image
 import face_recognition
-import os
-from datetime import datetime
-
-#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
+import requests
+
+# Set up Streamlit
+st.title("Face Recognition App")
 
+# 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:
@@ -37,58 +28,50 @@ def findEncodings(Images):
         encodeList.append(encode)
     return encodeList
 
-
 encodeListknown = findEncodings(Images)
 st.write('Encoding Complete')
 
-img_file_buffer=st.camera_input("Take a picture")
+# Take a picture using Streamlit camera input
+img_file_buffer = st.camera_input("Take a picture")
+
+# Check if an image was taken
 if img_file_buffer is not None:
-     
-     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)
+    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)
 
-     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)
+    # Find face locations and encodings
+    facesCurFrame = face_recognition.face_locations(imgS)
+    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
 
-         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)
+    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)
 
-             ##############
-             url = "https://rgiattendance.000webhostapp.com" 
-             url1 = "/update.php"
-             data1 =  {'name':name }
-             response = requests.post(url+url1, data=data1)
+        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)
 
-             # # https://aimljul23f.glitch.me/fsave?nm=testname
-             # url = "https://aimljul23f.glitch.me/" 
-             # url1 = "fsave?nm=" + name 
-             # # data1 =  {'name':name   } 
-             # response = requests.post(url+url1)
-             
-             if response.status_code == 200 : 
-                 st.write(" data updated on : " + url)
-             else : st.write("data NOT updated " + url+url1)
-                 
-     ##############################
-     st.image(image)
-if bytes_data is None:
-     st.stop()
+            # Update data using requests
+            url = "https://rgiattendance.000webhostapp.com/update.php"
+            data1 = {'name': name}
+            response = requests.post(url, data=data1)
 
+            if response.status_code == 200:
+                st.write("Data updated on: " + url)
+            else:
+                st.write("Data NOT updated " + url)
 
+    st.image(image)