Update app.py

app.py

@@ -1,95 +1,53 @@
 import os
-import face_recognition
-import cv2
-import smtplib
-from simple_salesforce import Salesforce
-from email.mime.text import MIMEText
-from email.mime.multipart import MIMEMultipart
-
-# Salesforce credentials
-USERNAME = 'ajayalways411@gmail.com'
-PASSWORD = 'Anusha@310819'
-SECURITY_TOKEN = 'DcroeUKt0Lc4Lpqgzh9dMxEyQ'
-
-# Connect to Salesforce
-sf = Salesforce(username=USERNAME, password=PASSWORD, security_token=SECURITY_TOKEN)
-
-# Query records from two custom objects (with image fields)
-custom_object_1 = 'CustomerData__c'
-custom_object_2 = 'staff__c'
-
-# Replace with actual field names
-query_1 = f"SELECT PhoneNumber__c, Name, AdhardCardPhoto__c FROM {custom_object_1}"
-query_2 = f"SELECT contactNumber__c, Name, AdhardCard__c FROM {custom_object_2}"
-
-# Retrieve records
-records_custom_object_1 = sf.query(query_1)['records']
-records_custom_object_2 = sf.query(query_2)['records']
-
-# Combine both objects' records
-salesforce_records = records_custom_object_1 + records_custom_object_2
-
-# Function to send an email if unknown face is found
-def send_email(unknown_image_path):
-    sender_email = "aajayk999@gmail.com@gmail.com"
-    receiver_email = "ajayaklaram07@gmail.com"
-    password = "8008995817"
-    
-    message = MIMEMultipart("alternative")
-    message["Subject"] = "Unknown Face Detected"
-    message["From"] = sender_email
-    message["To"] = receiver_email
-    
-    # Email body
-    text = f"An unknown face was detected in the image: {unknown_image_path}"
-    part1 = MIMEText(text, "plain")
-    message.attach(part1)
-    
-    # Send email
-    with smtplib.SMTP_SSL("smtp.gmail.com", 465) as server:
-        server.login(sender_email, password)
-        server.sendmail(sender_email, receiver_email, message.as_string())
-
-# Folder with input images to match
-input_folder = "C:\Users\nikit\OneDrive\Desktop\New folder (2)"
-
-# Function to compare images and find matches
-def compare_images(salesforce_records, input_folder):
-    for image_file in os.listdir(input_folder):
-        input_image_path = os.path.join(input_folder, image_file)
-        
-        # Load the input image
-        input_image = face_recognition.load_image_file(input_image_path)
-        input_encoding = face_recognition.face_encodings(input_image)
-
-        if len(input_encoding) == 0:
-            print(f"No face found in {image_file}")
-            continue
-
-        input_encoding = input_encoding[0]
-        
-        found_match = False
-        
-        # Compare input image to Salesforce records
-        for record in salesforce_records:
-            record_photo_url = record['PhotoField__c']
-            
-            # Load Salesforce image (you can download and use images from Salesforce as needed)
-            # Here, assuming the photos are locally available
-            salesforce_image = face_recognition.load_image_file(record_photo_url)
-            salesforce_encoding = face_recognition.face_encodings(salesforce_image)[0]
-            
-            # Compare the encodings
-            matches = face_recognition.compare_faces([salesforce_encoding], input_encoding)
-            
-            if True in matches:
-                print(f"Match found! Input image matches {record['Name']}")
-                found_match = True
-                break
-        
-        if not found_match:
-            print(f"Unknown face in {image_file}")
-            send_email(input_image_path)
-
-# Run the comparison function
-compare_images(salesforce_records, input_folder)
+from transformers import AutoModel, AutoTokenizer
+import torch
+from PIL import Image
+
+# Load pre-trained model and tokenizer
+model_name = "deepface/face-recognition"
+model = AutoModel.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define folders
+folder1 = "C:/Users/nikit/OneDrive/esktop/New folder (2)"
+folder2 = "C:/Users/nikit/OneDrive/Desktop/New folder"
+
+# Define threshold for matching (e.g., 0.5)
+threshold = 0.5
+
+# Function to extract face embeddings
+def extract_embeddings(folder):
+    embeddings = []
+    for file in os.listdir(folder):
+        image = Image.open(os.path.join(folder, file))
+        inputs = tokenizer(images=image, return_tensors='pt')
+        outputs = model(**inputs)
+        embeddings.append(outputs.last_hidden_state[:, 0, :].detach().numpy())
+    return embeddings
+
+# Extract embeddings from both folders
+embeddings1 = extract_embeddings(folder1)
+embeddings2 = extract_embeddings(folder2)
+
+# Compare embeddings
+matches = []
+for embedding1 in embeddings1:
+    for embedding2 in embeddings2:
+        similarity = torch.cosine_similarity(torch.tensor(embedding1), torch.tensor(embedding2))
+        if similarity > threshold:
+            matches.append((embedding1, embedding2))
+
+# Print matches
+for match in matches:
+    print(f"Match found: {match[0]} (Folder 1) and {match[1]} (Folder 2)")
+
+# Print non-matches
+non_matches = []
+for embedding1 in embeddings1:
+    for embedding2 in embeddings2:
+        similarity = torch.cosine_similarity(torch.tensor(embedding1), torch.tensor(embedding2))
+        if similarity < threshold:
+            non_matches.append((embedding1, embedding2))
+
+for non_match in non_matches:
+    print(f"No match found: {non_match[0]} (Folder 1) and {non_match[1]} (Folder 2)")