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app.py

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+from flask import Flask, jsonify, request
+from flask_cors import CORS
+import numpy as np
+import matplotlib.pyplot as plt
+import base64
+import io
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+from PIL import Image
+from torchvision import transforms
+from ultralytics import YOLO
+
+
+from PIL import Image
+app = Flask(__name__)
+CORS(app)
+
+
+idx_to_class_resnet50 = {0 : "Genuine" , 1:'Printed Paper' , 2 : 'Replayed'}
+idx_to_class_yolo9 = idx_to_class_yolo9 = {0: 'Genuine', 1: 'Printed Paper', 2: 'Replayed', 3: 'Paper Mask'}
+idx_to_class_resnet50_celeba = {0 : "Genuine" , 1:'Printed Paper' , 2 : 'Paper Cut',3:'Replayed',4:'3D Mask'}
+transform_data_resnet50=transforms.Compose([
+    transforms.Resize(size=(224,224)),
+    transforms.ToTensor()
+])
+
+transform_data_resnet50_celeba=transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Resize((224,224), antialias=True)
+])
+
+model_resnet50 = models.resnet50(weights=False)
+num_classes = 3 
+model_resnet50.fc = nn.Linear(model_resnet50.fc.in_features, num_classes)
+model_resnet50.load_state_dict(torch.load('resnet50_pytorch_rose_weights.pth',map_location=torch.device('cpu')))
+model_resnet50.eval()
+
+model_resnet50_celeba = models.resnet50(weights=False)
+num_classes = 5
+model_resnet50_celeba.fc = nn.Linear(model_resnet50_celeba.fc.in_features, num_classes)
+model_resnet50_celeba.load_state_dict(torch.load('resnet50_model_weights_celeba.pth',map_location=torch.device('cpu')))
+model_resnet50_celeba.eval()
+
+model_yolo9 = YOLO('yolo9_best.pt')
+print('Models Loaded Successfully')
+
+@app.route('/')
+def home():
+    return "Welcome to the Flask API!"
+
+
+@app.route('/api/data', methods=['GET'])
+def get_data():
+    img = plt.imread('test1.jpeg')
+    img_arr = np.array(img)
+    pil_img = Image.fromarray(img_arr.astype(np.uint8))  
+    buffered = io.BytesIO()
+    pil_img.save(buffered, format="JPEG")
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+
+    data = {
+        'message': 'Hello, World!',
+        'items': [1, 2, 3, 4, 5],
+        'image': img_str
+    }
+    return jsonify(data)
+
+@app.route('/api/data', methods=['POST'])
+def post_data():
+    data = request.json
+    base64_image = data['imageData']
+    filename = data.get('filename', 'image.jpg')
+    image_data = base64.b64decode(base64_image)
+    image = Image.open(io.BytesIO(image_data)).convert('RGB')
+    image.save(filename)
+    if data['model']=='resnet':
+        transform_img = transform_data_resnet50(image).unsqueeze(0)
+        with torch.no_grad():
+            pred = model_resnet50(transform_img)
+            probabilities = F.softmax(pred[0], dim=0)
+            cat = torch.argmax(pred[0]).item()
+            prob = round((probabilities[cat] * 100).item(),2)
+            name = idx_to_class_resnet50[cat]
+    elif data['model']=='resnet50':
+        transform_img = transform_data_resnet50_celeba(image).unsqueeze(0)
+        with torch.no_grad():
+            pred = model_resnet50_celeba(transform_img)
+            probabilities = F.softmax(pred[0], dim=0)
+            cat = torch.argmax(pred[0]).item()
+            prob = round((probabilities[cat] * 100).item(),2)
+            name = idx_to_class_resnet50_celeba[cat]
+    else:
+        results = model_yolo9(image)
+        name = 'not detectable'
+        prob = 0.00
+        for result in results[0].boxes:
+            cls = int(result.cls.item())
+            name = idx_to_class_yolo9[cls]
+            prob = round(result.conf.item() * 100,2)
+    
+    response = {
+        'message': 'Data received!',
+        'your_base64': data['imageData'],
+        'class' : name,
+        'prob' : prob
+    }
+    return jsonify(response), 201
+
+if __name__ == '__main__':
+    app.run(debug=True,port = 5000)

resnet50_model_weights_celeba.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:426540175980acb7aac2d61cc89a41abab9f10244efccf40ae6d11f07e08edb0
+size 94395630

resnet50_pytorch_rose_weights.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b4479730c838e12351b22add968e84b8dbe4480a0131f650c152106d96c0e478
+size 94381514

yolo9_best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2a288ba20d964edc8a694ca2ae2705f87a51508a47f982eb35e4fd65507aa9b8
+size 51548923