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Dockerfile

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+FROM python:3.11-bookworm
+
+COPY requirements.txt requirements.txt
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+      bzip2 \
+      g++ \
+      git \
+      graphviz \
+      libgl1-mesa-glx \
+      libhdf5-dev \
+      openmpi-bin \
+      wget \
+      python3-tk && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN pip install --upgrade pip
+
+RUN pip install --no-cache-dir -r requirements.txt
+
+RUN useradd -m -u 1000 myuser
+
+USER myuser
+
+COPY --chown=myuser app app
+
+EXPOSE 8001
+
+CMD ["python", "app/main.py"]

README.md

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----
-title: FacRecog
-emoji: ⚡
-colorFrom: red
-colorTo: blue
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: FaceSimilarity
+emoji: 🏢
+colorFrom: green
+colorTo: indigo
+sdk: docker
+pinned: false
+license: mit
+short_description: Face Similarity application
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app/Hackathon_setup/exp_recognition.py

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+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+import torch
+# In the below line,remove '.' while working on your local system.However Make sure that '.' is present before face_recognition_model while uploading to the server, Do not remove it.
+from .exp_recognition_model import *
+from PIL import Image
+import base64
+import io
+import os
+## Add more imports if required
+
+#############################################################################################################################
+#   Caution: Don't change any of the filenames, function names and definitions                                              #
+#   Always use the current_path + file_name for refering any files, without it we cannot access files on the server         #
+#############################################################################################################################
+
+# Current_path stores absolute path of the file from where it runs.
+current_path = os.path.dirname(os.path.abspath(__file__))
+
+
+#1) The below function is used to detect faces in the given image.
+#2) It returns only one image which has maximum area out of all the detected faces in the photo.
+#3) If no face is detected,then it returns zero(0).
+
+def detected_face(image):
+    eye_haar = current_path + '/haarcascade_eye.xml'
+    face_haar = current_path + '/haarcascade_frontalface_default.xml'
+    face_cascade = cv2.CascadeClassifier(face_haar)
+    eye_cascade = cv2.CascadeClassifier(eye_haar)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+    face_areas=[]
+    images = []
+    required_image=0
+    for i, (x,y,w,h) in enumerate(faces):
+        face_cropped = gray[y:y+h, x:x+w]
+        face_areas.append(w*h)
+        images.append(face_cropped)
+        required_image = images[np.argmax(face_areas)]
+        required_image = Image.fromarray(required_image)
+    return required_image
+    
+
+#1) Images captured from mobile is passed as parameter to the below function in the API call, It returns the Expression detected by your network.
+#2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function.
+#3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode.
+#4) Perform necessary transformations to the input(detected face using the above function), this should return the Expression in string form ex: "Anger"
+#5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function 
+##Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
+def get_expression(img):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    ##########################################################################################
+    ##Example for loading a model using weight state dictionary:                            ##
+    ## face_det_net = facExpRec() #Example Network                                          ##
+    ## model = torch.load(current_path + '/exp_recognition_net.t7', map_location=device)    ##
+    ## face_det_net.load_state_dict(model['net_dict'])                                      ##
+    ##                                                                                      ##
+    ##current_path + '/<network_definition>' is path of the saved model if present in       ##
+    ##the same path as this file, we recommend to put in the same directory                 ##
+    ##########################################################################################
+    ##########################################################################################
+
+    face = detected_face(img)
+    if face==0:
+        face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
+    
+    # YOUR CODE HERE, return expression using your model
+
+    return "YET TO BE CODED"

app/Hackathon_setup/exp_recognition_model.py

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+import torch
+import torchvision
+import torch.nn as nn
+from torchvision import transforms
+## Add more imports if required
+
+####################################################################################################################
+# Define your model and transform and all necessary helper functions here                                          #
+# They will be imported to the exp_recognition.py file                                                             #
+####################################################################################################################
+
+# Definition of classes as dictionary
+classes = {0: 'ANGER', 1: 'DISGUST', 2: 'FEAR', 3: 'HAPPINESS', 4: 'NEUTRAL', 5: 'SADNESS', 6: 'SURPRISE'}
+
+# Example Network
+class facExpRec(torch.nn.Module):
+    def __init__(self):
+        pass   # remove 'pass' once you have written your code
+        #YOUR CODE HERE
+        
+    def forward(self, x):
+        pass   # remove 'pass' once you have written your code
+        #YOUR CODE HERE
+        
+# Sample Helper function
+def rgb2gray(image):
+    return image.convert('L')
+    
+# Sample Transformation function
+#YOUR CODE HERE for changing the Transformation values.
+trnscm = transforms.Compose([rgb2gray, transforms.Resize((48,48)), transforms.ToTensor()])

app/Hackathon_setup/face_recognition.py

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+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+import torch
+# In the below line,remove '.' while working on your local system. However Make sure that '.' is present before face_recognition_model while uploading to the server, Do not remove it.
+from .face_recognition_model import *
+from PIL import Image
+import base64
+import io
+import os
+import joblib
+import pickle
+# Add more imports if required
+
+
+
+###########################################################################################################################################
+#         Caution: Don't change any of the filenames, function names and definitions                                                      #
+#        Always use the current_path + file_name for refering any files, without it we cannot access files on the server                  # 
+###########################################################################################################################################
+
+# Current_path stores absolute path of the file from where it runs. 
+current_path = os.path.dirname(os.path.abspath(__file__))
+# --- GLOBAL SETUP: Must match your training transforms ---
+# Define the transformation pipeline for inference
+trnscm = transforms.Compose([
+    transforms.Grayscale(num_output_channels=1),
+    transforms.Resize((100, 100)),
+    transforms.ToTensor()
+])
+CLASS_NAMES = ['Person0', 'Person1', 'Person2', 'Person3', 'Person4'] # ADJUST THIS!
+
+# --- Model Filenames ---
+SIAMESE_MODEL_PATH = current_path + '/siamese_model.t7'
+KNN_CLASSIFIER_PATH = current_path + '/decision_tree_model.sav'
+SCALER_PATH = current_path + '/face_recognition_scaler.sav'
+
+#1) The below function is used to detect faces in the given image.
+#2) It returns only one image which has maximum area out of all the detected faces in the photo.
+#3) If no face is detected,then it returns zero(0).
+
+def detected_face(image):
+    eye_haar = current_path + '/haarcascade_eye.xml'
+    face_haar = current_path + '/haarcascade_frontalface_default.xml'
+    face_cascade = cv2.CascadeClassifier(face_haar)
+    eye_cascade = cv2.CascadeClassifier(eye_haar)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
+    face_areas=[]
+    images = []
+    required_image=0
+    for i, (x,y,w,h) in enumerate(faces):
+        face_cropped = gray[y:y+h, x:x+w]
+        face_areas.append(w*h)
+        images.append(face_cropped)
+        required_image = images[np.argmax(face_areas)]
+        required_image = Image.fromarray(required_image)
+    return required_image
+
+
+#1) Images captured from mobile is passed as parameter to the below function in the API call. It returns the similarity measure between given images.
+#2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function.
+#3) Define an object to your siamese network here in the function and load the weight from the trained network, set it in evaluation mode.
+#4) Get the features for both the faces from the network and return the similarity measure, Euclidean,cosine etc can be it. But choose the Relevant measure.
+#5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function 
+#Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
+def get_similarity(img1, img2):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    det_img1 = detected_face(img1)
+    det_img2 = detected_face(img2)
+    if(det_img1 == 0 or det_img2 == 0):
+        det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+        det_img2 = Image.fromarray(cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY))
+    face1 = trnscm(det_img1).unsqueeze(0)
+    face2 = trnscm(det_img2).unsqueeze(0)
+    ##########################################################################################
+    ##Example for loading a model using weight state dictionary:                            ##
+    ## feature_net = light_cnn() #Example Network                                           ##
+    ## model = torch.load(current_path + '/siamese_model.t7', map_location=device)          ##
+    ## feature_net.load_state_dict(model['net_dict'])                                       ##
+    ##                                                                                      ##
+    ##current_path + '/<network_definition>' is path of the saved model if present in       ##
+    ##the same path as this file, we recommend to put in the same directory                 ##
+    ##########################################################################################
+    ##########################################################################################
+    
+    # YOUR CODE HERE, load the model
+    
+    # YOUR CODE HERE, return similarity measure using your model
+    # 1. Initialize and Load Siamese Network
+    try:
+        # Assuming your Siamese Network class is named 'SiameseNetwork'
+        siamese_net = SiameseNetwork().to(device)
+        siamese_net.load_state_dict(torch.load(SIAMESE_MODEL_PATH, map_location=device))
+        siamese_net.eval()
+    except Exception as e:
+        print(f"Error loading Siamese Model get_similarity: {e}")
+        return -1 # Return error code
+
+    # 2. Get Features (Embeddings)
+    with torch.no_grad():
+        # Get the feature vector from one tower/forward_once method
+        # Ensure your SiameseNetwork class has a forward_once or get_embedding method
+        embed1 = siamese_net.forward_once(face1).cpu().numpy()
+        embed2 = siamese_net.forward_once(face2).cpu().numpy()
+        
+    # 3. Calculate Similarity Measure
+    # The Euclidean distance is the fundamental metric used by the Triplet/Contrastive loss.
+    # We return the NEGATIVE Euclidean distance or COSINE similarity, as *higher* value usually means *more* similar.
+    
+    # Option A: Euclidean Distance (Lower is better) -> return NEGATIVE distance for API expectation
+    # distance = euclidean_distances(embed1, embed2)[0][0]
+    # similarity = -distance 
+    
+    # Option B: Cosine Similarity (Higher is better) -> Recommended
+    similarity = cosine_similarity(embed1, embed2)[0][0]
+    
+    return float(similarity)
+    
+#1) Image captured from mobile is passed as parameter to this function in the API call, It returns the face class in the string form ex: "Person1"
+#2) The image is passed to the function in base64 encoding, Code to decode the image provided within the function
+#3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode
+#4) Perform necessary transformations to the input(detected face using the above function).
+#5) Along with the siamese, you need the classifier as well, which is to be finetuned with the faces that you are training
+##Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
+def get_face_class(img1):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    det_img1 = detected_face(img1)
+    if(det_img1 == 0):
+        det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+    ##YOUR CODE HERE, return face class here
+    ##Hint: you need a classifier finetuned for your classes, it takes o/p of siamese as i/p to it
+    ##Better Hint: Siamese experiment is covered in one of the labs
+    face1_tensor = trnscm(det_img1).unsqueeze(0).to(device)
+
+    # 1. Load Siamese Network (Feature Extractor)
+    try:
+        siamese_net = SiameseNetwork().to(device)
+        siamese_net.load_state_dict(torch.load(SIAMESE_MODEL_PATH, map_location=device))
+        siamese_net.eval()
+    except Exception as e:
+        return f"Error loading Siamese Model get_face_class: {e}"
+
+    # 2. Extract Embedding
+    with torch.no_grad():
+        embedding_np = siamese_net.forward_once(face1_tensor).cpu().numpy()
+    
+    # 3. Load Sklearn Scaler and Classifier (Joblib)
+    try:
+        knn_classifier = joblib.load(KNN_CLASSIFIER_PATH)
+        scaler = joblib.load(SCALER_PATH)
+    except Exception as e:
+        return f"Error loading Sklearn models: {e}"
+    
+    # 4. Preprocess Embedding and Predict
+    # The embedding must be reshaped to (1, N_features) for the scaler
+    embedding_scaled = scaler.transform(embedding_np.reshape(1, -1))
+    
+    # Perform prediction (returns a NumPy array with the predicted label index)
+    predicted_label_index = knn_classifier.predict(embedding_scaled)[0]
+    
+    # 5. Map index to Class Name
+    if predicted_label_index < len(CLASS_NAMES):
+        predicted_class_name = CLASS_NAMES[predicted_label_index]
+    else:
+        predicted_class_name = "UNKNOWN_CLASS"
+
+    return predicted_class_name

app/Hackathon_setup/face_recognition_model.py

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+import math
+import torch
+import torchvision
+import torch.nn as nn
+import torch.nn.functional as F
+from torchvision import transforms
+# Add more imports if required
+
+# Sample Transformation function
+# YOUR CODE HERE for changing the Transformation values.
+trnscm = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()])
+
+##Example Network
+class Siamese(torch.nn.Module):
+    def __init__(self):
+        super(Siamese, self).__init__()
+        #YOUR CODE HERE
+        
+    def forward(self, x):
+        pass   # remove 'pass' once you have written your code
+        #YOUR CODE HERE
+        
+##########################################################################################################
+## Sample classification network (Specify if you are using a pytorch classifier during the training)    ##
+## classifier = nn.Sequential(nn.Linear(64, 64), nn.BatchNorm1d(64), nn.ReLU(), nn.Linear...)           ##
+##########################################################################################################
+
+# YOUR CODE HERE for pytorch classifier
+
+# Definition of classes as dictionary
+classes = ['person1','person2','person3','person4','person5','person6','person7']
+def get_similarity(img1, img2):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    det_img1 = detected_face(img1)
+    det_img2 = detected_face(img2)
+    if(det_img1 == 0 or det_img2 == 0):
+        det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+        det_img2 = Image.fromarray(cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY))
+    face1 = trnscm(det_img1).unsqueeze(0)
+    face2 = trnscm(det_img2).unsqueeze(0)
+    ##########################################################################################
+    ##Example for loading a model using weight state dictionary:                            ##
+    ## feature_net = light_cnn() #Example Network                                           ##
+    ## model = torch.load(current_path + '/siamese_model.t7', map_location=device)          ##
+    ## feature_net.load_state_dict(model['net_dict'])                                       ##
+    ##                                                                                      ##
+    ##current_path + '/<network_definition>' is path of the saved model if present in       ##
+    ##the same path as this file, we recommend to put in the same directory                 ##
+    ##########################################################################################
+    ##########################################################################################
+    
+    # YOUR CODE HERE, load the model
+    
+    # YOUR CODE HERE, return similarity measure using your model
+    # 1. Initialize and Load Siamese Network
+    try:
+        # Assuming your Siamese Network class is named 'SiameseNetwork'
+        siamese_net = SiameseNetwork().to(device)
+        siamese_net.load_state_dict(torch.load(SIAMESE_MODEL_PATH, map_location=device))
+        siamese_net.eval()
+    except Exception as e:
+        print(f"Error loading Siamese Model: {e}")
+        return -1 # Return error code
+
+    # 2. Get Features (Embeddings)
+    with torch.no_grad():
+        # Get the feature vector from one tower/forward_once method
+        # Ensure your SiameseNetwork class has a forward_once or get_embedding method
+        embed1 = siamese_net.forward_once(face1).cpu().numpy()
+        embed2 = siamese_net.forward_once(face2).cpu().numpy()
+        
+    # 3. Calculate Similarity Measure
+    # The Euclidean distance is the fundamental metric used by the Triplet/Contrastive loss.
+    # We return the NEGATIVE Euclidean distance or COSINE similarity, as *higher* value usually means *more* similar.
+    
+    # Option A: Euclidean Distance (Lower is better) -> return NEGATIVE distance for API expectation
+    # distance = euclidean_distances(embed1, embed2)[0][0]
+    # similarity = -distance 
+    
+    # Option B: Cosine Similarity (Higher is better) -> Recommended
+    similarity = cosine_similarity(embed1, embed2)[0][0]
+    
+    return float(similarity)
+    
+def get_face_class(img1):
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    
+    det_img1 = detected_face(img1)
+    if(det_img1 == 0):
+        det_img1 = Image.fromarray(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY))
+    ##YOUR CODE HERE, return face class here
+    ##Hint: you need a classifier finetuned for your classes, it takes o/p of siamese as i/p to it
+    ##Better Hint: Siamese experiment is covered in one of the labs
+    face1_tensor = trnscm(det_img1).unsqueeze(0).to(device)
+
+    # 1. Load Siamese Network (Feature Extractor)
+    try:
+        siamese_net = SiameseNetwork().to(device)
+        siamese_net.load_state_dict(torch.load(SIAMESE_MODEL_PATH, map_location=device))
+        siamese_net.eval()
+    except Exception as e:
+        return f"Error loading Siamese Model get_face_class: {e}"
+
+    # 2. Extract Embedding
+    with torch.no_grad():
+        embedding_np = siamese_net.forward_once(face1_tensor).cpu().numpy()
+    
+    # 3. Load Sklearn Scaler and Classifier (Joblib)
+    try:
+        knn_classifier = joblib.load(KNN_CLASSIFIER_PATH)
+        scaler = joblib.load(SCALER_PATH)
+    except Exception as e:
+        return f"Error loading Sklearn models: {e}"
+    
+    # 4. Preprocess Embedding and Predict
+    # The embedding must be reshaped to (1, N_features) for the scaler
+    embedding_scaled = scaler.transform(embedding_np.reshape(1, -1))
+    
+    # Perform prediction (returns a NumPy array with the predicted label index)
+    predicted_label_index = knn_classifier.predict(embedding_scaled)[0]
+    
+    # 5. Map index to Class Name
+    if predicted_label_index < len(CLASS_NAMES):
+        predicted_class_name = CLASS_NAMES[predicted_label_index]
+    else:
+        predicted_class_name = "UNKNOWN_CLASS"
+
+    return predicted_class_name

app/Hackathon_setup/hackathon4a_face_similarity_and_recognition (1).py

@@ -0,0 +1,818 @@
+# -*- coding: utf-8 -*-
+"""Hackathon4a_Face_Similarity_and_Recognition.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-Gc2gRNagx-DUpMULf7O-GMFdM8526qW
+
+# Advanced Certification in AIML
+## A Program by IIIT-H and TalentSprint
+
+Face recognition is a method of identifying or verifying the identity of an individual using their face. Face recognition systems can be used to identify people in photos, video, or in real-time. The recent major breakthrough in increasing the face recognition accuracy has come from advancements in Deep Learning and CNNs.
+
+**Objectives:**
+
+**Stage 1:** Build a Siamese Network and obtain the Siamese Representation for the AT&T faces dataset
+
+**Stage 2 (10 Marks):** Use the same Siamese Model to deploy the application for Face Similarity on HuggingFace spaces
+
+**Stage 3 (10 Marks):** Get the Siamese Network Representation of the Team Data and build a classifier to perform Face Recognition
+
+##**Stage 1: Build a Siamese Network**
+
+
+---
+
+
+* Define a Siamese network and obtain Siamese Representation on the AT&T Faces Dataset (code given)
+
+**Dataset download**
+
+The [AT&T Faces Dataset](https://git-disl.github.io/GTDLBench/figures/faces.gif) contains 10 different images of each of 40 distinct persons. Images were taken with variations in times, lighting, facial expressions and facial details (eg. glasses / no glasses).
+
+Dataset Statistics: Color: Grayscale; Sample Size: 92x112; Total Samples: 400; Dataset Size: 4.5 MB (compressed in .tar.z)
+"""
+
+#@title #####Download the AT&T Dataset.
+
+import os
+import urllib.request
+import zipfile
+
+def setup():
+    # Download AT&T dataset
+    if not os.path.exists("data-20190607T005435Z-001.zip"):
+        print("Downloading AT&T dataset...")
+        urllib.request.urlretrieve("https://cdn.talentsprint.com/aiml/FaceRecogHackathon/Datasets/ATandT/data-20190607T005435Z-001.zip", "data-20190607T005435Z-001.zip")
+
+    # Extract dataset
+    if not os.path.exists("data"):
+        print("Extracting dataset...")
+        with zipfile.ZipFile("data-20190607T005435Z-001.zip", 'r') as zip_ref:
+            zip_ref.extractall(".")
+
+    # Download face cascade
+    if not os.path.exists("lbpcascade_frontalface.xml"):
+        print("Downloading face cascade...")
+        urllib.request.urlretrieve("https://cdn.iisc.talentsprint.com/AIandMLOps/Datasets/lbpcascade_frontalface.xml", "lbpcascade_frontalface.xml")
+
+    print("Setup completed successfully")
+    return
+
+setup()
+
+import os
+print("Current directory contents:")
+for item in os.listdir('.'):
+    print(item)
+
+"""#### **Import Required Packages**
+
+"""
+
+# Commented out IPython magic to ensure Python compatibility.
+# %matplotlib inline
+import torchvision
+import torchvision.datasets as dset
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader,Dataset
+import matplotlib.pyplot as plt
+import torchvision.utils
+import numpy as np
+import random
+import cv2
+from PIL import Image       # PIL (Pillow) is the Python Image Library. Used to cut and resize images, or do simple manipulation.
+import torch
+from torch.autograd import Variable
+import PIL.ImageOps
+import torch.nn as nn
+from torch import optim
+import torch.nn.functional as F
+
+"""**Helper functions**"""
+
+## The below function plots a given tensor image
+def imshow(img,text=None,should_save=False):
+    npimg = img.numpy()
+    plt.axis("off")
+    if text:
+        plt.text(75, 8, text, style='italic',fontweight='bold', bbox={'facecolor':'white', 'alpha':0.8, 'pad':10})
+    plt.imshow(np.transpose(npimg, (1, 2, 0)))
+
+    # In PyTorch, the order of dimension is "channel*width*height" but in matplotlib it is "width*height*channel".
+    # So a transpose is performed to correctly index the dimensions
+    plt.imshow(np.transpose(npimg, (1, 2, 0)))
+    plt.show()
+
+## We will use the below method to plot the loss graph while training
+def show_plot(iteration,loss):
+    plt.plot(iteration,loss)
+    plt.show()
+
+"""**Configuration class**"""
+
+# A simple class to manage all configurations
+class Config():
+    training_dir = "./data/faces/training/"
+    testing_dir = "./data/faces/testing/"
+    train_batch_size = 64
+    train_number_epochs = 100
+
+"""**Display a sample image**"""
+
+import cv2
+import matplotlib.pyplot as plt
+
+# Opencv's imread takes full path of an image as input to read an image
+im1 = cv2.imread('./data/faces/training/s1/10.pgm')
+print(im1.shape)
+plt.imshow(cv2.cvtColor(im1, cv2.COLOR_BGR2RGB))
+plt.axis('off')
+plt.show()
+
+"""#### **Define a custom SiameseNetworkDataset class**
+
+This dataset generates a pair of images. 0 for geniune pair (similar) and 1 for imposter pair (dissimilar)
+"""
+
+# Below is the class of custom dataset which arranges images in pairs and gives the labels. if both are same, then label will be 0 otherwise 1
+# As our images are in .pgm extension (portable gray map) we converting them 'L' to store luminance which is basically single channel image.
+
+class SiameseNetworkDataset(Dataset):
+    def __init__(self,imageFolderDataset, transform=None):
+        self.imageFolderDataset = imageFolderDataset
+        self.transform = transform
+
+    # Overriding the data retriever (__getitem__) to provide a pair of images + similar/dissimilar label
+    def __getitem__(self,index):
+        img0_tuple = random.choice(self.imageFolderDataset.imgs)
+        #we need to make sure approx 50% of images are in the same class
+        should_get_same_class = random.randint(0,1)
+        if should_get_same_class:
+            while True:
+                #keep looping till the same class image is found
+                img1_tuple = random.choice(self.imageFolderDataset.imgs)
+                if img0_tuple[1]==img1_tuple[1]:
+                    break
+        else:
+            while True:
+                #keep looping till a different class image is found
+                img1_tuple = random.choice(self.imageFolderDataset.imgs)
+                if img0_tuple[1] !=img1_tuple[1]:
+                    break
+
+        img0 = Image.open(img0_tuple[0])
+        img1 = Image.open(img1_tuple[0])
+        img0 = img0.convert("L")
+        img1 = img1.convert("L")
+
+        if self.transform is not None:
+            img0 = self.transform(img0)
+            img1 = self.transform(img1)
+
+        return img0, img1 , torch.from_numpy(np.array([int(img1_tuple[1]!=img0_tuple[1])],dtype=np.float32))
+
+    def __len__(self):
+        return len(self.imageFolderDataset.imgs)
+
+"""**Create an Image Folder Dataset**"""
+
+folder_dataset = dset.ImageFolder(root=Config.training_dir)
+
+"""**Create the Siamese Network Dataset**"""
+
+# Create the object for the SiameseNetworkDataset class (defined earlier in this notebook);
+
+# NOTE: the 'TRANSFORMS' HERE CONSISTS OF
+# a) resizing to 100*100
+# b) Converting to tensor.
+
+# YOU HAVE TO APPLY THE SAME TRANSORMS WHEN DEPLOYING THE MODEL ON THE SERVER!!
+
+siamese_dataset = SiameseNetworkDataset(imageFolderDataset = folder_dataset, transform = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()]))
+
+"""**Exclude malformed images to avoid image loading errors** (Use Nonechucks)"""
+
+# In cases where your dataloader gives 'image not found' error i.e.
+# if an image is malformed, nonechucks helps in eliminating those images so that your dataloader doesn't fail.
+# So good practice to use this code below
+
+#!pip install Nonechucks
+#import nonechucks as nc
+
+## The below line will avoid loading the image files that are corrupted or unreadable.
+#siamese_dataset = nc.SafeDataset(siamese_dataset)
+
+"""**Visualising some of the data**
+The top row and the bottom row of any column is one pair. The 0s and 1s correspond to the column of the image.
+1 indiciates dissimilar, and 0 indicates similar.
+"""
+
+vis_dataloader = DataLoader(siamese_dataset, shuffle=True, num_workers=8, batch_size=8)
+
+dataiter = iter(vis_dataloader)
+
+example_batch = next(dataiter)
+concatenated = torch.cat((example_batch[0],example_batch[1]),0)
+imshow(torchvision.utils.make_grid(concatenated))
+print(example_batch[2].numpy())
+
+"""#### **Neural Net Definition**
+
+We will use a standard convolutional neural network
+"""
+
+class SiameseNetwork(nn.Module):
+    def __init__(self):
+        super(SiameseNetwork, self).__init__()
+        self.cnn1 = nn.Sequential(
+            nn.ReflectionPad2d(1),       #Pads the input tensor using the reflection of the input boundary, it similar to the padding.
+            nn.Conv2d(1, 4, kernel_size=3),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(4),
+
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(4, 8, kernel_size=3),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(8),
+
+
+            nn.ReflectionPad2d(1),
+            nn.Conv2d(8, 8, kernel_size=3),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm2d(8),
+        )
+
+        self.fc1 = nn.Sequential(
+            nn.Linear(8*100*100, 500),
+            nn.ReLU(inplace=True),
+
+            nn.Linear(500, 500),
+            nn.ReLU(inplace=True),
+
+            nn.Linear(500, 5))
+
+    # forward_once is for one image. This can be used while classifying the face images
+    def forward_once(self, x):
+        output = self.cnn1(x)
+        output = output.view(output.size()[0], -1)
+        output = self.fc1(output)
+        return output
+
+    def forward(self, input1, input2):
+        output1 = self.forward_once(input1)
+        output2 = self.forward_once(input2)
+        return output1, output2
+
+"""**Contrastive Loss**"""
+
+class ContrastiveLoss(torch.nn.Module):
+    """
+    Contrastive loss function.
+    Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
+    """
+
+    def __init__(self, margin=2.0):
+        super(ContrastiveLoss, self).__init__()
+        self.margin = margin
+
+    def forward(self, output1, output2, label):
+        euclidean_distance = F.pairwise_distance(output1, output2, keepdim = True)
+        loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance, 2) +
+                                      (label) * torch.pow(torch.clamp(self.margin - euclidean_distance, min=0.0), 2))
+
+        return loss_contrastive
+
+"""**Training the model**"""
+
+train_dataloader = DataLoader(siamese_dataset, shuffle=True, num_workers=8, batch_size=Config.train_batch_size)
+
+# Check if CUDA is available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+net = SiameseNetwork().to(device)
+criterion = ContrastiveLoss()
+optimizer = optim.Adam(net.parameters(), lr=0.0005)
+
+counter = []
+loss_history = []
+iteration_number= 0
+
+# Training loop
+print("Starting training...")
+for epoch in range(0, Config.train_number_epochs):
+    epoch_loss = 0
+    for i, data in enumerate(train_dataloader, 0):
+        img0, img1, label = data
+        img0, img1, label = img0.to(device), img1.to(device), label.to(device)
+
+        optimizer.zero_grad()
+        output1, output2 = net(img0, img1)
+        loss_contrastive = criterion(output1, output2, label)
+        loss_contrastive.backward()
+        optimizer.step()
+
+        epoch_loss += loss_contrastive.item()
+
+        if i % 10 == 0:
+            print("Epoch number {}\n Current loss {}\n".format(epoch, loss_contrastive.item()))
+            iteration_number += 10
+            counter.append(iteration_number)
+            loss_history.append(loss_contrastive.item())
+
+    avg_loss = epoch_loss / len(train_dataloader)
+    print(f"Epoch {epoch} completed. Average loss: {avg_loss:.4f}")
+
+print("Training completed!")
+
+# Plotting the loss graph using the function show_plot
+show_plot(counter,loss_history)
+
+"""**Saving and loading model**
+
+https://pytorch.org/tutorials/beginner/saving_loading_models.html
+"""
+
+## Saving the model as a state dictionary
+state = {'net_dict': net.state_dict()}
+torch.save(state, './siamese_model.t7')
+
+"""**Verifying the saved model with load_state_dict**"""
+
+myModel = SiameseNetwork().to(device)
+
+# Commented out IPython magic to ensure Python compatibility.
+# %ls
+
+ckpt = torch.load('./siamese_model.t7')
+
+# Save the state dictionary of the Siamese network (use pytorch only), It will be useful in integrating to the mobile app
+# A state_dict is simply a Python dictionary object that maps each layer of the network to its parameters (weights)
+# As a Python dictionary it can be easily saved, updated, altered and restored, adding a great deal of modularity to PyTorch models
+
+myModel.load_state_dict(ckpt['net_dict'])
+
+"""##### Create the siamese dataset for the `testing_dir` and also the respective data loader. The Distance between each image pair denotes the degree of similarity the model found between the two images. Less means it found the images more similar, while higher values indicate it found them to be dissimilar."""
+
+folder_dataset_test = dset.ImageFolder(root = Config.testing_dir) #testing_dir
+siamese_dataset = SiameseNetworkDataset(imageFolderDataset = folder_dataset_test,
+                                        transform = transforms.Compose([transforms.Resize((100,100)), transforms.ToTensor()]))
+
+##siamese_dataset = nc.SafeDataset(siamese_dataset)
+
+test_dataloader = DataLoader(siamese_dataset,num_workers=6,batch_size=1,shuffle=True)
+dataiter = iter(test_dataloader)
+x0,_,_ = next(dataiter)
+
+# Check the similarity for the first few images from the test data loader
+for i in range(10):
+    _,x1,label2 = next(dataiter)
+    concatenated = torch.cat((x0,x1),0)
+
+    output1,output2 = myModel(Variable(x0).to(device),Variable(x1).to(device)) #using the loaded 'myModel'; even 'net' can be used
+    #..but using 'myModel' helps a quick test on if the save and load model is working fine. You will replicate the same
+    #..in the server.
+
+    euclidean_distance = F.pairwise_distance(output1, output2)
+    imshow(torchvision.utils.make_grid(concatenated),'Dissimilarity: {:.2f}'.format(euclidean_distance.item()))
+
+"""##**Stage 2: Deploy your Face Similarity application on HuggingFace Spaces (10 Marks)**
+
+
+---
+
+1. [Sign up](https://huggingface.co/join) for a Hugging Face account
+
+2. Once you have an account, create an access token for your account and save it:
+    
+    - Go to your `Settings`, then click on the `Access Tokens` tab. Click on the `New token` button to create a new User Access Token.
+    - Select a role(`write`) and a name for your token
+    - Click Generate a token
+
+    To know more about Access Tokens, refer [here](https://huggingface.co/docs/hub/security-tokens).
+
+3. Start a new Hugging Face Space by going to your profile and [clicking "New Space"](https://huggingface.co/new-space)
+
+4. Provide details for your space:
+    - Space name
+    - License (eg. [MIT](https://opensource.org/licenses/MIT))
+    - For Space SDK (software development kit), select `Docker` (**It is mandatory to select *Docker* for this application to work.**)
+    - Choose a Docker template: `Blank`
+    - Space hardware (CPU basic)
+    - Choose whether your Space is public or private
+    - Click "Create Space"
+    - After Space creation, the files of your repo can be seen via `Files` tab
+
+5. Clone your space repository on to your system
+    - Go to 3-dots icon beside Settings, then select `Clone Repository` option
+        - `git lfs install`
+        - `git clone <your-repo>`
+    - Run clone commands in your terminal (Provide your HuggingFace username and access token if prompted for authentication.)
+
+6. Download the project folder **`recognition_api`**, shared along with this notebook, on to your system
+
+7. Add below files from **`recognition_api`** folder to your cloned repository folder
+    - `app`, `requirements.txt`, `Dockerfile`, and `.gitattributes`
+    - You may need to replace the `.gitattributes` file if it is already present in repository
+    - **NOTE:** Do NOT commit and push changes yet.
+
+8. Update files present in your cloned repository
+    - Download the Siamese model(trained in colab for face similarity) and place it within **`app/Hackathon_setup/`** folder
+    - Update the Siamese model architecture in the **`app/Hackathon_setup/face_recognition_model.py`** file and provide the code in the **`get_similarity()`** function of the **`app/Hackathon_setup/face_recognition.py`** file. (See Deployment related files)
+    - Update the **`README.md`** file and include `app_port: 8001`
+
+9. Commit your changes and push to HuggingFace Space repository
+
+10. Access the `App` tab of your repository to see the build progress (debug if error persists) [This step might take sometime.]
+
+11. Once the app has built successfully, you should see below message
+
+    `Application startup complete. Uvicorn running on http://0.0.0.0:8001`
+
+12. Test the Siamese model using the application running on your Space
+
+    Go to 3-dots icon beside Settings, then select `Embed this Space` option, and go to `Direct URL`
+    - Select the task, `Face Similarity` for now
+    - Select 'Send Anyway' when prompted
+    - Upload images and test
+    - **NOTE:** When using the Direct URL link via mobile, the camera option will also enable to capture images (Set 1:1 aspect ratio in camera settings before-hand)
+
+##**Stage 3: Build Face Recognition Classification Model with Siamese Representation of Team Data and Deploy on HuggingFace Spaces (10 Marks)**
+
+
+---
+
+
+- Build a Face Recognition Classification Model on Team data
+    - Collect Team data by running the code cells provided below
+        - The collected data images will be stored in the `captured_face_images` directory
+
+        - NOTE: *Since team members will be using separate colab notebooks, they can capture their own face images and then download it and share with other members for model training.* Code cell is provided below to download the data.
+
+    - Train a classifier (use any classifier) with the features extracted from the above trained Siamese network, for your Team Data
+    - Save the Classification Model with joblib (if Sklearn classification model is used)
+
+- Deploy on your HuggingFace Space
+
+    - Download the trained models (Siamese model, Classification model) and upload them in your HuggingFace Space repository within **`app/Hackathon_setup/`** folder
+    
+    - Update the Siamese model architecture in the **`app/Hackathon_setup/face_recognition_model.py`** file (If Required)
+    
+    - Update the code in the **`get_face_class()`** function of the **`app/Hackathon_setup/face_recognition.py`** file. (See Deployment related files)
+
+- Access the `App` tab of your repository to see the build progress (debug if error persists) [This step might take sometime.]
+
+- Once the app has built successfully, you should see below message
+
+    `Application startup complete. Uvicorn running on http://0.0.0.0:8001`
+
+- Test the model's Face Recognition functionality using the application running on your Space
+
+    Go to 3-dots icon beside Settings, then select `Embed this Space` option, and go to `Direct URL`
+    - Select the task, `Face Recognition`
+    - Select 'Send Anyway' when prompted
+    - Upload your image and test
+    - **NOTE:** When using the Direct URL link via mobile, the camera option will also enable to capture images (Set 1:1 aspect ratio in camera settings before-hand)
+"""
+
+# @title Run this cell to Setup Image Capturing in Colab {display-mode: "form"}
+
+from IPython.display import display, Javascript
+from google.colab.output import eval_js
+from base64 import b64decode
+from PIL import Image
+import imageio
+import datetime
+import pathlib
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+AREA_THRESHOLD = 2304
+
+def take_photo(filename='photo.jpg', quality=0.8):
+  js = Javascript('''
+    async function takePhoto(quality) {
+      const div = document.createElement('div');
+      const capture = document.createElement('button');
+      capture.textContent = 'Capture';
+      div.appendChild(capture);
+
+      const video = document.createElement('video');
+      video.style.display = 'block';
+      const stream = await navigator.mediaDevices.getUserMedia({video: true});
+
+      document.body.appendChild(div);
+      div.appendChild(video);
+      video.srcObject = stream;
+      await video.play();
+
+      // Resize the output to fit the video element.
+      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);
+
+      // Wait for Capture to be clicked.
+      await new Promise((resolve) => capture.onclick = resolve);
+
+      const canvas = document.createElement('canvas');
+      canvas.width = video.videoWidth;
+      canvas.height = video.videoHeight;
+      canvas.getContext('2d').drawImage(video, 0, 0);
+      stream.getVideoTracks()[0].stop(); // Stop the video stream
+      div.remove();
+      return canvas.toDataURL('image/jpeg', quality);
+    }
+    ''')
+  display(js)
+  data = eval_js('takePhoto({})'.format(quality))
+  binary = b64decode(data.split(',')[1])
+  with open(filename, 'wb') as f:
+    f.write(binary)
+
+  im = Image.open(filename)
+  im1 = im.crop((80, 0, 560, 480))
+  im1.save(filename)
+
+  return filename
+
+
+def save_faces(miniframe, filepath):
+    TRAINSET = "lbpcascade_frontalface.xml"
+    classifier = cv2.CascadeClassifier(TRAINSET)
+    faces = classifier.detectMultiScale(miniframe)
+    image = get_large_face(miniframe, faces)
+    if not isinstance(image, np.ndarray):
+        return {"status" : False}
+    plt.imshow(image)
+    plt.show()
+    #cv2.imwrite(filepath, image)
+    imageio.imwrite(filepath, image)
+    return {"status" : True}
+
+def get_large_face(miniframe, faces):
+    images = []
+    face_areas = []
+    required_image = 0
+    for x,y,w,h in faces:
+        face_cropped = miniframe[y:y+h, x:x+w]
+        face_areas.append(w*h)
+        images.append(face_cropped)
+        required_image = images[np.argmax(face_areas)]
+    if not face_areas:
+        return 0
+    if face_areas[np.argmax(face_areas)] < AREA_THRESHOLD:
+        return 0
+
+    return required_image
+
+def save_image(filename, class_name):
+    base_path = "captured_face_images/"
+
+    pathlib.Path(base_path + class_name).mkdir(parents=True, exist_ok=True)
+    file_name = class_name + "_" + datetime.datetime.now().strftime("%s") + ".jpg"
+    filepath = base_path +class_name + "/" + file_name
+
+    image = Image.open(filename)
+    miniframe = np.asarray(image)
+    status = save_faces(miniframe, filepath)
+    if status['status']:
+        print("Image saved in " + filepath, flush = True)
+    else:
+        print("Face not found!\nRetry!")
+
+# @title Capture an Image for Person1 $ $  [Re-run this cell to capture another image] {display-mode: "form"}
+
+class_name = "Person1"
+
+from IPython.display import Image as IPyImage
+import time
+
+num_images_to_capture = 5 # You can change this number to capture more or less images
+
+for i in range(num_images_to_capture):
+  print(f"Capturing image {i+1}/{num_images_to_capture} for {class_name}...")
+  try:
+    filename = take_photo()
+    save_image(filename, class_name)
+  except Exception as err:
+    print(str(err))
+  time.sleep(1) # Add a small delay between captures
+
+# @title Capture an Image for Person2 $ $  [Re-run this cell to capture another image] {display-mode: "form"}
+
+class_name = "Person2"
+
+from IPython.display import Image as IPyImage
+try:
+  filename = take_photo()
+  #print('Saved to {}'.format(filename))
+  # Show the image which was just taken.
+  #display(IPyImage(filename))
+except Exception as err:
+  # Errors will be thrown if the user does not have a webcam or if they do not
+  # grant the page permission to access it.
+  print(str(err))
+
+save_image(filename, class_name)
+
+# @title Capture an Image for Person3 $ $  [Re-run this cell to capture another image] {display-mode: "form"}
+
+class_name = "Person3"
+
+from IPython.display import Image as IPyImage
+try:
+  filename = take_photo()
+  #print('Saved to {}'.format(filename))
+  # Show the image which was just taken.
+  #display(IPyImage(filename))
+except Exception as err:
+  # Errors will be thrown if the user does not have a webcam or if they do not
+  # grant the page permission to access it.
+  print(str(err))
+
+save_image(filename, class_name)
+
+# @title Capture an Image for Person4 $ $  [Re-run this cell to capture another image] {display-mode: "form"}
+
+class_name = "Person4"
+
+from IPython.display import Image as IPyImage
+try:
+  filename = take_photo()
+  #print('Saved to {}'.format(filename))
+  # Show the image which was just taken.
+  #display(IPyImage(filename))
+except Exception as err:
+  # Errors will be thrown if the user does not have a webcam or if they do not
+  # grant the page permission to access it.
+  print(str(err))
+
+save_image(filename, class_name)
+
+# @title Capture an Image for Person5 $ $  [Re-run this cell to capture another image] {display-mode: "form"}
+
+class_name = "Person5"
+
+from IPython.display import Image as IPyImage
+try:
+  filename = take_photo()
+  #print('Saved to {}'.format(filename))
+  # Show the image which was just taken.
+  #display(IPyImage(filename))
+except Exception as err:
+  # Errors will be thrown if the user does not have a webcam or if they do not
+  # grant the page permission to access it.
+  print(str(err))
+
+save_image(filename, class_name)
+
+# @title Capture an Image for Person6 $ $  [Re-run this cell to capture another image] {display-mode: "form"}
+
+class_name = "Person6"
+
+from IPython.display import Image as IPyImage
+try:
+  filename = take_photo()
+  #print('Saved to {}'.format(filename))
+  # Show the image which was just taken.
+  #display(IPyImage(filename))
+except Exception as err:
+  # Errors will be thrown if the user does not have a webcam or if they do not
+  # grant the page permission to access it.
+  print(str(err))
+
+save_image(filename, class_name)
+
+# @title Capture an Image for Person7 $ $  [Re-run this cell to capture another image] {display-mode: "form"}
+
+class_name = "Person7"
+
+from IPython.display import Image as IPyImage
+try:
+  filename = take_photo()
+  #print('Saved to {}'.format(filename))
+  # Show the image which was just taken.
+  #display(IPyImage(filename))
+except Exception as err:
+  # Errors will be thrown if the user does not have a webcam or if they do not
+  # grant the page permission to access it.
+  print(str(err))
+
+save_image(filename, class_name)
+
+# @title Download Collected Images $ $ [OPTIONAL]  {display-mode: "form"}
+
+from google.colab import files
+!zip -r "captured_face_images.zip" "captured_face_images"
+files.download('captured_face_images.zip')
+print("Downloaded captured_face_images.zip !!")
+
+!cd captured_face_images && ls -a
+
+"""**While uploading the team images manually to captured_face_images. this file .ipynb_checkpoints will be created and make issue to delet run the below code**"""
+
+rm -rf `find -type d -name .ipynb_checkpoints`
+
+# Here simple ImageFolder is enough; we don't need SiameseDataSet
+finalClassifierDset = dset.ImageFolder(root='./captured_face_images',
+                                       transform = transforms.Compose([transforms.Grayscale(num_output_channels = 1), transforms.Resize((100,100)), transforms.ToTensor()]))
+
+# Dataloader using the dataset created above.
+representation_dataloader = DataLoader(finalClassifierDset, shuffle=False, num_workers=8, batch_size=100)
+
+# Load the state dict of the siamese model
+siamese_model = SiameseNetwork().to(device)
+ckpt = torch.load('./siamese_model.t7', map_location=device)
+siamese_model.load_state_dict(ckpt['net_dict'])
+siamese_model.eval()
+print("Siamese model loaded successfully!")
+
+# Get a siamese representation of each of your data points i.e. for each of your team images.
+siamese_features = []
+labels = []
+
+print("Extracting features from team images...")
+with torch.no_grad():
+    for batch_idx, (data, target) in enumerate(representation_dataloader):
+        data = data.to(device)
+        # Get features using the siamese network
+        features = siamese_model.forward_once(data)
+        siamese_features.append(features.cpu().numpy())
+        labels.extend(target.numpy())
+
+# Convert to numpy arrays
+X = np.vstack(siamese_features)
+y = np.array(labels)
+
+print(f"Feature shape: {X.shape}")
+print(f"Labels shape: {y.shape}")
+print(f"Unique labels: {np.unique(y)}")
+
+"""**Train a Classifier for Face Recognition**
+
+You can use any classifier with the features extracted from the above trained Siamese network of your team data. If required, you have to convert torch variable to numpy array before using SkLearn.
+"""
+
+# Train a classifier using sklearn
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, classification_report
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train Random Forest classifier
+classifier = RandomForestClassifier(n_estimators=100, random_state=42)
+classifier.fit(X_train, y_train)
+
+# Make predictions
+y_pred = classifier.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+
+print(f"Classification Accuracy: {accuracy:.4f}")
+print("\nClassification Report:")
+print(classification_report(y_test, y_pred))
+
+"""**Save your classification model**
+
+* Save your sklearn models using joblib
+
+
+"""
+
+# Save the classification model using joblib
+import joblib
+
+# Save the classifier
+joblib.dump(classifier, 'face_classifier_model.sav')
+print("Classification model saved as 'face_classifier_model.sav'")
+
+# Also save the class names for reference
+class_names = ['Person1', 'Person2', 'Person3', 'Person4', 'Person5', 'Person6', 'Person7']
+joblib.dump(class_names, 'class_names.sav')
+print("Class names saved as 'class_names.sav'")
+
+"""**Download your trained model using the code below**
+* Given the path of model file the following code downloads it through the browser
+"""
+
+# Download the trained models
+import os
+
+# Check if files exist and download them
+model_files = ['siamese_model.t7', 'face_classifier_model.sav', 'class_names.sav']
+
+for file in model_files:
+    if os.path.exists(file):
+        print(f"Model file '{file}' is ready for download")
+    else:
+        print(f"Warning: Model file '{file}' not found")
+
+# Note: In a local environment, you can manually copy these files
+# In Google Colab, you would use:
+# from google.colab import files
+# files.download('siamese_model.t7')
+# files.download('face_classifier_model.sav')
+# files.download('class_names.sav')
+

app/Hackathon_setup/lbpcascade_frontalface.xml

@@ -0,0 +1,1505 @@
+<?xml version="1.0"?>
+<!--
+number of positive samples 3000
+number of negative samples 1500
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier">
+  <stageType>BOOST</stageType>
+  <featureType>LBP</featureType>
+  <height>24</height>
+  <width>24</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>0.9950000047683716</minHitRate>
+    <maxFalseAlarm>0.5000000000000000</maxFalseAlarm>
+    <weightTrimRate>0.9500000000000000</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>256</maxCatCount></featureParams>
+  <stageNum>20</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>3</maxWeakCount>
+      <stageThreshold>-0.7520892024040222</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
+            0 -1 46 -67130709 -21569 -1426120013 -1275125205 -21585
+            -16385 587145899 -24005</internalNodes>
+          <leafValues>
+            -0.6543210148811340 0.8888888955116272</leafValues></_>
+        <!-- tree 1 -->
+        <_>
+          <internalNodes>
+            0 -1 13 -163512766 -769593758 -10027009 -262145 -514457854
+            -193593353 -524289 -1</internalNodes>
+          <leafValues>
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+        <!-- tree 2 -->
+        <_>
+          <internalNodes>
+            0 -1 2 -363936790 -893203669 -1337948010 -136907894
+            1088782736 -134217726 -741544961 -1590337</internalNodes>
+          <leafValues>
+            -0.7068563103675842 0.6761534214019775</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-0.4872078299522400</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 1 -->
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+          <leafValues>
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+        <!-- tree 2 -->
+        <_>
+          <internalNodes>
+            0 -1 106 -98110272 1610939566 -285484400 -850010381
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+          <leafValues>
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+        <!-- tree 3 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>4</maxWeakCount>
+      <stageThreshold>-1.1592328548431396</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
+            0 -1 47 -21585 -20549 -100818262 -738254174 -20561 -36865
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+          <leafValues>
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+        <!-- tree 1 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 2 -->
+        <_>
+          <internalNodes>
+            0 -1 26 -50347012 970882927 -50463492 -1253377 -134218251
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+          <leafValues>
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+        <!-- tree 3 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-0.7562355995178223</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <!-- tree 1 -->
+        <_>
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+          <leafValues>
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+        <!-- tree 2 -->
+        <_>
+          <internalNodes>
+            0 -1 57 -12817 -49 -541103378 -152950 -38993 -20481 -1153876
+            -72478976</internalNodes>
+          <leafValues>
+            -0.6582943797111511 0.5339496731758118</leafValues></_>
+        <!-- tree 3 -->
+        <_>
+          <internalNodes>
+            0 -1 125 -269484161 -452984961 -319816180 -1594032130 -2111
+            -990117891 -488975296 -520947741</internalNodes>
+          <leafValues>
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+        <!-- tree 4 -->
+        <_>
+          <internalNodes>
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+            1998528318 1056964607 -33570977 -1</internalNodes>
+          <leafValues>
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+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-0.8085358142852783</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 1 -->
+        <_>
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+          <leafValues>
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+        <!-- tree 2 -->
+        <_>
+          <internalNodes>
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+            -167903241 -1 -1</internalNodes>
+          <leafValues>
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+        <!-- tree 3 -->
+        <_>
+          <internalNodes>
+            0 -1 44 -32777 1006582562 -65 935312171 -8388609 -1078198273
+            -1 733886267</internalNodes>
+          <leafValues>
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+        <!-- tree 4 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-0.5549971461296082</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <!-- tree 1 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <!-- tree 2 -->
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <!-- tree 3 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 4 -->
+        <_>
+          <internalNodes>
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+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>5</maxWeakCount>
+      <stageThreshold>-0.8776460289955139</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
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+        <!-- tree 1 -->
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+        <!-- tree 2 -->
+        <_>
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+          <leafValues>
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+        <!-- tree 3 -->
+        <_>
+          <internalNodes>
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+        <!-- tree 4 -->
+        <_>
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+    <!-- stage 7 -->
+    <_>
+      <maxWeakCount>6</maxWeakCount>
+      <stageThreshold>-1.1139287948608398</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
+        <_>
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+        <!-- tree 2 -->
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+        <!-- tree 3 -->
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+        <!-- tree 4 -->
+        <_>
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+        <!-- tree 5 -->
+        <_>
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+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>7</maxWeakCount>
+      <stageThreshold>-0.8243625760078430</stageThreshold>
+      <weakClassifiers>
+        <!-- tree 0 -->
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+        <!-- tree 2 -->
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+        <!-- tree 3 -->
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+        <!-- tree 5 -->
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+    <!-- stage 9 -->
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+    <!-- stage 10 -->
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+    <!-- stage 11 -->
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+    <!-- stage 12 -->
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+    <!-- stage 13 -->
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+    <!-- stage 14 -->
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+    <!-- stage 15 -->
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+</opencv_storage>

app/Hackathon_setup/siamese_model.t7

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+version https://git-lfs.github.com/spec/v1
+oid sha256:51f94087f49e39d1fa0f093e4d1469a4c8807a8b1d20abcf4b70b5f7fcecd27a
+size 161027032

app/__init__.py

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+__version__ = "0.0.1"

app/config.py

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+import sys
+from typing import List
+
+from pydantic import AnyHttpUrl, BaseSettings
+
+class Settings(BaseSettings):
+    API_V1_STR: str = "/api/v1"
+
+    # Meta
+ 
+    # BACKEND_CORS_ORIGINS is a comma-separated list of origins
+    # e.g: http://localhost,http://localhost:4200,http://localhost:3000
+    BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = [
+        "http://localhost:3000",  # type: ignore
+        "http://localhost:8000",  # type: ignore
+        "https://localhost:3000",  # type: ignore
+        "https://localhost:8000",  # type: ignore
+    ]
+
+    PROJECT_NAME: str = "Recognition API"
+
+    class Config:
+        case_sensitive = True
+
+settings = Settings()

app/main.py

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+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+#print(sys.path)
+from typing import Any
+
+from fastapi import FastAPI, Request, APIRouter, File, UploadFile
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from fastapi.middleware.cors import CORSMiddleware
+from app.config import settings
+from app import __version__
+from app.Hackathon_setup import face_recognition, exp_recognition
+
+import numpy as np
+from PIL import Image
+
+
+app = FastAPI(
+    title=settings.PROJECT_NAME, openapi_url=f"{settings.API_V1_STR}/openapi.json"
+)
+
+# To store files uploaded by users
+app.mount("/static", StaticFiles(directory="app/static"), name="static")
+
+# To access Templates directory
+templates = Jinja2Templates(directory="app/templates")
+
+simi_filename1 = None
+simi_filename2 = None
+face_rec_filename = None
+expr_rec_filename = None
+
+
+####################################  Home Page endpoints  #################################################
+@app.get("/")
+async def root(request: Request):
+    return templates.TemplateResponse("index.html", {'request': request,})
+
+
+####################################  Face Similarity endpoints  #################################################
+@app.get("/similarity/")
+async def similarity_root(request: Request):
+    return templates.TemplateResponse("similarity.html", {'request': request,})
+
+
+@app.post("/predict_similarity/")
+async def create_upload_files(request: Request, file1: UploadFile = File(...), file2: UploadFile = File(...)):
+    global simi_filename1
+    global simi_filename2
+    
+    if 'image' in file1.content_type:
+        contents = await file1.read()
+        simi_filename1 = 'app/static/' + file1.filename
+        with open(simi_filename1, 'wb') as f:
+            f.write(contents)
+    
+    if 'image' in file2.content_type:
+        contents = await file2.read()
+        simi_filename2 = 'app/static/' + file2.filename
+        with open(simi_filename2, 'wb') as f:
+            f.write(contents)
+    
+    img1 = Image.open(simi_filename1)
+    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+    
+    img2 = Image.open(simi_filename2)
+    img2 =  np.array(img2).reshape(img2.size[1], img2.size[0], 3).astype(np.uint8)
+    
+    result = face_recognition.get_similarity(img1, img2)
+    #print(result)
+    
+    return templates.TemplateResponse("predict_similarity.html", {"request": request,
+                                                       "result": np.round(result, 3),
+                                                       "simi_filename1": '../static/'+file1.filename,
+                                                       "simi_filename2": '../static/'+file2.filename,})
+    
+
+####################################  Face Recognition endpoints  #################################################
+@app.get("/face_recognition/")
+async def face_recognition_root(request: Request):
+    return templates.TemplateResponse("face_recognition.html", {'request': request,})
+
+
+@app.post("/predict_face_recognition/")
+async def create_upload_files(request: Request, file3: UploadFile = File(...)):
+    global face_rec_filename
+    
+    if 'image' in file3.content_type:
+        contents = await file3.read()
+        face_rec_filename = 'app/static/' + file3.filename
+        with open(face_rec_filename, 'wb') as f:
+            f.write(contents)
+    
+    img1 = Image.open(face_rec_filename)
+    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+        
+    result = face_recognition.get_face_class(img1)
+    print(result)
+    
+    return templates.TemplateResponse("predict_face_recognition.html", {"request": request,
+                                                       "result": result,
+                                                       "face_rec_filename": '../static/'+file3.filename,})
+
+
+####################################  Expresion Recognition endpoints  #################################################
+@app.get("/expr_recognition/")
+async def expr_recognition_root(request: Request):
+    return templates.TemplateResponse("expr_recognition.html", {'request': request,})
+
+
+@app.post("/predict_expr_recognition/")
+async def create_upload_files(request: Request, file4: UploadFile = File(...)):
+    global expr_rec_filename
+    
+    if 'image' in file4.content_type:
+        contents = await file4.read()
+        expr_rec_filename = 'app/static/' + file4.filename
+        with open(expr_rec_filename, 'wb') as f:
+            f.write(contents)
+    
+    img1 = Image.open(expr_rec_filename)
+    img1 =  np.array(img1).reshape(img1.size[1], img1.size[0], 3).astype(np.uint8)
+        
+    result = exp_recognition.get_expression(img1)
+    print(result)
+    
+    return templates.TemplateResponse("predict_expr_recognition.html", {"request": request,
+                                                       "result": result,
+                                                       "expr_rec_filename": '../static/'+file4.filename,})
+
+
+
+# Set all CORS enabled origins
+if settings.BACKEND_CORS_ORIGINS:
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=[str(origin) for origin in settings.BACKEND_CORS_ORIGINS],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+
+# Start app
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)

app/templates/expr_recognition.html

@@ -0,0 +1,32 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Index</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Expression Recognition</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+        <ul>
+            <!li>
+            <br>
+            <form action="/predict_expr_recognition/" enctype="multipart/form-data" method="post">
+                <span style="font-weight:bold;font-family:sans-serif">Upload Image:</span> <br><br>
+                <input name="file4" type="file" onchange="readURL(this);" />
+                <br><br><br>
+                <button type="submit">Recognize Expression</button>
+            </form>
+            <!/li>
+            <br><br>
+            <form action="/" method="get">
+                <button type="submit">Home</button>
+            </form>
+        </ul>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/face_recognition.html

@@ -0,0 +1,32 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Index</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Face Recognition</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+        <ul>
+            <!li>
+            <br>
+            <form action="/predict_face_recognition/" enctype="multipart/form-data" method="post">
+                <span style="font-weight:bold;font-family:sans-serif">Upload Image:</span> <br><br>
+                <input name="file3" type="file" onchange="readURL(this);" />
+                <br><br><br>
+                <button type="submit">Recognize Face</button>
+            </form>
+            <!/li>
+            <br><br>
+            <form action="/" method="get">
+                <button type="submit">Home</button>
+            </form>
+        </ul>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/index.html

@@ -0,0 +1,29 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Index</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Recognition Application</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+        <ul>
+            <li><span style="font-weight:bold;font-family:sans-serif">Select a task:</span> 
+            <br><br><br>
+            <form action="{{ url_for('similarity_root') }}"><button>Face Similarity</button></form>
+            <br><br>
+            <form action="{{ url_for('face_recognition_root') }}"><button>Face Recognition</button></form>
+            <br><br>
+            <form action="{{ url_for('expr_recognition_root') }}"><button>Expression Recognition</button></form>
+            <br>
+            </li>
+            <br>
+        </ul>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/predict_expr_recognition.html

@@ -0,0 +1,37 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Predict</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Expression Recognition</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+            <h2>
+                <center>
+                    <span style="font-weight:bold;font-family:sans-serif">Prediction: </span> 
+                    <span style="font-weight:bold;color:blue"> {{result}}</span>
+                </center>
+            </h2>
+            <h3><center><span style="font-weight:bold;font-family:sans-serif">Input image:</span></Input></center></h3> 
+            <p>
+              <center>
+              <img src="{{expr_rec_filename}}" alt={{expr_rec_filename1}} width='150' height='150'>
+              </center>
+            </p>
+            <br>
+            <form action="/expr_recognition/" method="get">
+                <center><button type="submit">Check Another Input</button></center>
+            </form>
+            <br>
+            <form action="/" method="get">
+                <center><button type="submit">Home</button></center>
+            </form>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/predict_face_recognition.html

@@ -0,0 +1,37 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Predict</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Face Recognition</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+            <h2>
+                <center>
+                    <span style="font-weight:bold;font-family:sans-serif">Prediction: </span> 
+                    <span style="font-weight:bold;color:blue"> {{result}}</span>
+                </center>
+            </h2>
+            <h3><center><span style="font-weight:bold;font-family:sans-serif">Input image:</span></Input></center></h3> 
+            <p>
+              <center>
+              <img src="{{face_rec_filename}}" alt={{face_rec_filename1}} width='150' height='150'>
+              </center>
+            </p>
+            <br>
+            <form action="/face_recognition/" method="get">
+                <center><button type="submit">Check Another Input</button></center>
+            </form>
+            <br>
+            <form action="/" method="get">
+                <center><button type="submit">Home</button></center>
+            </form>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/predict_similarity.html

@@ -0,0 +1,38 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Predict</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Face Similarity</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+            <h2>
+                <center>
+                    <span style="font-weight:bold;font-family:sans-serif">Dissimilarity:</span> 
+                    <span style="font-weight:bold;color:blue"> {{result}}</span>
+                </center>
+            </h2>
+            <h3><center><span style="font-weight:bold;font-family:sans-serif">Input images:</span></Input></center></h3> 
+            <p>
+              <center>
+              <img src="{{simi_filename1}}" alt={{simi_filename1}} width='150' height='150'>
+              <img src="{{simi_filename2}}" alt={{simi_filename2}} width='150' height='150'>
+              </center>
+            </p>
+            <br>
+            <form action="/similarity/" method="get">
+                <center><button type="submit">Check Another Input</button></center>
+            </form>
+            <br>
+            <form action="/" method="get">
+                <center><button type="submit">Home</button></center>
+            </form>
+        </fieldset>
+    </div>
+</body>
+</html>

app/templates/similarity.html

@@ -0,0 +1,35 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <title>Index</title>
+</head>
+<body>
+    <div>
+        <h1 style="background-color:LightGray;">
+            <center>Face Similarity</center>
+        </h1>
+    </div>
+    <div>
+        <fieldset>
+        <ul>
+            <!li>
+            <br>
+            <form action="/predict_similarity/" enctype="multipart/form-data" method="post">
+                <span style="font-weight:bold;font-family:sans-serif">Upload First Image:</span> <br><br>
+                <input name="file1" type="file" onchange="readURL(this);" />
+                <br><br><br>
+                <span style="font-weight:bold;font-family:sans-serif">Upload Second Image:</span> <br><br>
+                <input name="file2" type="file" onchange="readURL(this);" />
+                <br><br><br><br>
+                <button type="submit">Check Similarity</button>
+            </form>
+            <!/li>
+            <br><br>
+            <form action="/" method="get">
+                <button type="submit">Home</button>
+            </form>
+        </ul>
+        </fieldset>
+    </div>
+</body>
+</html>

requirements.txt

@@ -0,0 +1,19 @@
+uvicorn==0.17.6
+fastapi==0.99.1
+pydantic==1.10.10
+requests==2.23.0
+jinja2==3.1.2
+python-multipart==0.0.6
+
+scikit-learn==1.2.2
+joblib==1.3.2
+Pillow==9.4.0
+torch==2.1.0
+torchvision==0.16.0
+matplotlib==3.7.1
+
+#opencv-python==4.5.5.64
+numpy==1.26.4
+pandas==2.2.2
+# Prefer headless on servers (no GUI deps); 4.9 works well with numpy 1.26.x
+opencv-python-headless==4.9.0.80