Update app.py

app.py

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+import streamlit as st
+from PIL import Image
+import face_recognition
+import cv2
+import numpy as np
+import requests
+import os
+
+st.title("AIMLJan24 - Face Recognition")
+
+# create list of encoding of all images in photos folder
+# Load images for face recognition
+Images = []   # List to store Images
+classnames = []  # List to store classnames
+directory = "photos"
+myList = os.listdir(directory)
+
+st.write("Photographs found in folder : ")
+for cls in myList:
+    if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]:
+        img_path = os.path.join(directory, cls)
+        curImg = cv2.imread(img_path)
+        Images.append(curImg)
+        st.write(os.path.splitext(cls)[0])
+        classnames.append(os.path.splitext(cls)[0])
+
+# Load images for face recognition
+encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]
+
+# camera to take photo of user in question
+file_name = st.camera_input("Take a picture")  #st.file_uploader("Upload image  ")
+
+# Function to update Aadhaar data
+def update_data(name):
+    # url = "https://attendanceviaface.000webhostapp.com"
+    # url1 = "/update.php"
+    # data = {'name': name, 'aadhaar': '998877'}
+    # response = requests.post(url + url1, data=data)
+
+    url = "https://aimljan24f1.glitch.me/adduserdata"  #?rollno=222&name="+name
+    data = {'rollno':'222','name': name}
+    response = requests.post(url , data=data )
+    
+    if response.status_code == 200:
+        st.success("Data updated on: " + url)
+    else:
+        st.warning("Data not updated")
+
+if file_name is not None:
+    col1, col2 = st.columns(2)
+
+    test_image = Image.open(file_name)
+    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)
+
+    name = "Unknown"  # Default name for unknown faces
+    match_found = False  # Flag to track if a match is found
+
+    # Checking if faces are detected
+    if len(encodesCurFrame) > 0:
+        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
+            # Assuming that encodeListknown is defined and populated in your code
+            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()
+                match_found = True  # Set the flag to True
+                
+            y1, x2, y2, x1 = faceLoc
+            y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4)
+            
+            # Make a copy of the image array before drawing on it
+            image_copy = image.copy()
+            
+            cv2.rectangle(image_copy, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.rectangle(image_copy, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
+            cv2.putText(image_copy, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+
+            # update the database
+            update_data(name)
+            
+        st.image(image_copy, use_column_width=True, output_format="PNG")
+    else:
+        st.warning("No faces detected in the image. Face recognition failed.")
+
+    # image = Image.open(file_name)
+    # col1.image(image, use_column_width=True)
+
+# pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+# st.title("AIMLJan24 First App on Hugging face - Hot Dog? Or Not?")
+
+# file_name = st.file_uploader("Upload the test image to find is this hot dog ! ")
+
+# if file_name is not None:
+#     col1, col2 = st.columns(2)
+
+#     image = Image.open(file_name)
+#     col1.image(image, use_column_width=True)
+#     predictions = pipeline(image)
+
+#     col2.header("Probabilities")
+#     for p in predictions:
+#         col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+
+
+# # my first app
+# import streamlit as st
+
+# x = st.slider('Select a value')
+# st.write(x, 'squared is', x * x)