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diff --git a/Model/lung_cancer_detection_model.onnx b/Model/lung_cancer_detection_model.onnx
new file mode 100644
index 0000000000000000000000000000000000000000..a914ea112be56f42c92e31e9f3ff915263e44002
--- /dev/null
+++ b/Model/lung_cancer_detection_model.onnx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ff06083f190a9c4f261cd3e62cfa626d873a6dbc70f1ab9e8f54857490a585f
+size 96055618
diff --git a/Model/lung_cancer_detection_model.pth b/Model/lung_cancer_detection_model.pth
new file mode 100644
index 0000000000000000000000000000000000000000..619f185f106cc348d7fba3c368744f01bede8809
--- /dev/null
+++ b/Model/lung_cancer_detection_model.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9de85ca91279cb5f7dbecd8cf8255c0aecac235478801d3587f94d76d3a54495
+size 96458150
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..320be44f73342453c1bc6cbf8cffacf52cea5083
--- /dev/null
+++ b/README.md
@@ -0,0 +1,76 @@
+# LUNGAI: Lung Cancer Detection Model
+
+## Project Overview
+LungAI is a deep learning project aimed at detecting and classifying lung cancer from CT scan images. The model can differentiate between cancerous and non-cancerous lung tissue, as well as classify specific types of lung cancer.
+
+4x hackathon award winner - out of 1,500 total competitors.
+
+[![GitHub](https://img.shields.io/badge/-GitHub-181717?style=for-the-badge&logo=github)](https://github.com/DorsaRoh/LungAI)
+[![HuggingFace](https://img.shields.io/badge/-HuggingFace-FFFF00?style=for-the-badge&logo=huggingface)](https://huggingface.co/dorsar/lung-cancer-detection)
+
+
+## Model Performance
+- 98% accuracy in distinguishing between cancerous and non-cancerous cases
+- 83% accuracy in differentiating between four specific types of lung conditions:
+  - Adenocarcinoma: 82% F1-score
+  - Large Cell Carcinoma: 85% F1-score
+  - Normal (non-cancerous): 98% F1-score
+  - Squamous Cell Carcinoma: 76% F1-score
+
+<i>This project represents the newest version, now using PyTorch.</i>
+
+## Repository Structure
+- `Architecture/`: Contains the core model scripts
+  - `architecture.py`: Defines the model architecture
+  - `preprocess.py`: Data preprocessing utilities
+  - `test.py`: Script for testing the model
+- `Model/`: Stores trained model files
+  - `lung_cancer_detection_model.onnx`: ONNX format of the trained model
+  - `lung_cancer_detection_model.pth`: PyTorch weights of the trained model
+- `Data/`: (Not included in repository) Directory for storing the dataset
+- `Processed_Data/`: (Not included in repository) Directory for preprocessed data
+- `assets/`: Additional project assets
+- `requirements.txt`: List of Python dependencies
+
+
+## Setup and Usage
+
+### Step 1: Install Dependencies
+
+First, ensure you have Python installed. Then, install the required Python libraries using the following command:
+
+```bash
+pip install -r requirements.txt
+```
+
+### Step 2: Train the Model (Optional)
+
+Run the training script to train the model. 
+**It will be saved as `.pth` and `.onnx` files**
+
+```bash
+python Architecture/architecture.py
+```
+
+### Step 3: Run the Model
+
+Run the model by running the following file:
+
+```bash
+python Architecture/run.py
+```
+
+### Notes
+
+- Make sure your dataset is structured correctly under the Processed_Data directory with subdirectories for training, validation, and testing sets.
+- The model training script expects the dataset to be in the Processed_Data directory. Ensure that the data transformations and directory paths are correctly set up in architecture.py.
+
+### Contributing
+
+If you would like to contribute to this project, please fork the repository and submit a pull request. We welcome improvements, bug fixes, and new features.
+
+## Connect with Me
+
+[![GitHub](https://img.shields.io/badge/-GitHub-181717?style=for-the-badge&logo=github)](https://github.com/DorsaRoh)
+[![Twitter](https://img.shields.io/badge/-Twitter-1DA1F2?style=for-the-badge&logo=twitter)](https://twitter.com/Dorsa_Rohani)
+[![LinkedIn](https://img.shields.io/badge/-LinkedIn-0077B5?style=for-the-badge&logo=linkedin)](https://www.linkedin.com/in/dorsarohani/)
\ No newline at end of file
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..528a05e2eac8f0948f4d50f00893390bc43ee182
--- /dev/null
+++ b/app.py
@@ -0,0 +1,43 @@
+import torch
+from PIL import Image
+from torchvision import transforms
+from architecture import ResNetLungCancer
+import gradio as gr
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+model = ResNetLungCancer(num_classes=4)
+model.load_state_dict(torch.load('Model/lung_cancer_detection_model.pth', map_location=device))
+model = model.to(device)
+model.eval()
+
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+class_names = ['Adenocarcinoma', 'Large Cell Carcinoma', 'Normal', 'Squamous Cell Carcinoma']
+
+def predict(image):
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+    input_tensor = preprocess(image).unsqueeze(0).to(device)
+    
+    with torch.no_grad():
+        output = model(input_tensor)
+    
+    predicted_class = torch.argmax(output, dim=1).item()
+    return class_names[predicted_class]
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(),
+    outputs=gr.Label(num_top_classes=1),
+    examples=[
+        ["Data/test/large.cell.carcinoma/000108.png"],
+        ["Data/test/normal/7 - Copy (3).png"]
+    ]
+)
+
+iface.launch(share=True)
\ No newline at end of file
diff --git a/architecture.py b/architecture.py
new file mode 100644
index 0000000000000000000000000000000000000000..23f95032c445a5b438734c4fd3ea5c7a6deb15ba
--- /dev/null
+++ b/architecture.py
@@ -0,0 +1,178 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import datasets, transforms, models
+from torch.utils.data import DataLoader
+import os
+import copy
+from torch.optim.lr_scheduler import ReduceLROnPlateau
+from torchvision.models import resnet50, ResNet50_Weights
+import ssl
+ssl._create_default_https_context = ssl._create_unverified_context
+
+# data transformations with augmentation
+train_transform = transforms.Compose([
+    transforms.RandomResizedCrop(224),
+    transforms.RandomHorizontalFlip(),
+    transforms.RandomRotation(10),
+    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+val_test_transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+
+class ResNetLungCancer(nn.Module):
+    def __init__(self, num_classes, use_pretrained=True):
+        super(ResNetLungCancer, self).__init__()
+        if use_pretrained:
+            weights = ResNet50_Weights.IMAGENET1K_V1
+        else:
+            weights = None
+        self.resnet = resnet50(weights=weights)
+        num_ftrs = self.resnet.fc.in_features
+        self.resnet.fc = nn.Identity()  # remove the final fully connected layer
+        self.fc = nn.Sequential(
+            nn.Linear(num_ftrs, 256),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(256, num_classes)
+        )
+
+    def forward(self, x):
+        x = self.resnet(x)
+        return self.fc(x)
+
+
+# train function
+def train_model(model, train_loader, valid_loader, criterion, optimizer, scheduler, num_epochs=50, device='cuda'):
+    best_model_wts = copy.deepcopy(model.state_dict())
+    best_acc = 0.0
+
+    for epoch in range(num_epochs):
+        print(f'Epoch {epoch}/{num_epochs - 1}')
+        print('-' * 10)
+
+        for phase in ['train', 'valid']:
+            if phase == 'train':
+                model.train()
+                dataloader = train_loader
+            else:
+                model.eval()
+                dataloader = valid_loader
+
+            running_loss = 0.0
+            running_corrects = 0
+
+            for inputs, labels in dataloader:
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+
+                optimizer.zero_grad()
+
+                with torch.set_grad_enabled(phase == 'train'):
+                    outputs = model(inputs)
+                    _, preds = torch.max(outputs, 1)
+                    loss = criterion(outputs, labels)
+
+                    if phase == 'train':
+                        loss.backward()
+                        optimizer.step()
+
+                running_loss += loss.item() * inputs.size(0)
+                running_corrects += torch.sum(preds == labels.data)
+
+            epoch_loss = running_loss / len(dataloader.dataset)
+            epoch_acc = running_corrects.double() / len(dataloader.dataset)
+
+            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
+
+            if phase == 'valid':
+                scheduler.step(epoch_acc)
+                current_lr = optimizer.param_groups[0]['lr']
+                print(f'Learning rate: {current_lr}')
+                if epoch_acc > best_acc:
+                    best_acc = epoch_acc
+                    best_model_wts = copy.deepcopy(model.state_dict())
+
+        print()
+
+    print(f'Best val Acc: {best_acc:.4f}')
+    model.load_state_dict(best_model_wts)
+    return model
+
+# eval the model
+def evaluate_model(model, test_loader, device='cuda'):
+    model.eval()
+    running_corrects = 0
+
+    with torch.no_grad():
+        for inputs, labels in test_loader:
+            inputs = inputs.to(device)
+            labels = labels.to(device)
+
+            outputs = model(inputs)
+            _, preds = torch.max(outputs, 1)
+            running_corrects += torch.sum(preds == labels.data)
+
+    test_acc = running_corrects.double() / len(test_loader.dataset)
+    print(f'Test Acc: {test_acc:.4f}')
+
+if __name__ == "__main__":
+    # device
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    # data
+    data_dir = 'Processed_Data'
+    train_dataset = datasets.ImageFolder(os.path.join(data_dir, 'train'), transform=train_transform)
+    valid_dataset = datasets.ImageFolder(os.path.join(data_dir, 'valid'), transform=val_test_transform)
+    test_dataset = datasets.ImageFolder(os.path.join(data_dir, 'test'), transform=val_test_transform)
+
+    #  dataloaders
+    batch_size = 32
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
+    valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, num_workers=4)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)
+
+    print(f"Number of training images: {len(train_dataset)}")
+    print(f"Number of validation images: {len(valid_dataset)}")
+    print(f"Number of test images: {len(test_dataset)}")
+
+    # initialize model, loss, and optimizer
+    num_classes = len(train_dataset.classes)
+    model = ResNetLungCancer(num_classes)
+    model = model.to(device)
+
+    criterion = nn.CrossEntropyLoss()
+    
+    pretrained_params = list(model.resnet.parameters())
+    new_params = list(model.fc.parameters())
+
+    optimizer = optim.Adam([
+        {'params': pretrained_params, 'lr': 1e-5},
+        {'params': new_params, 'lr': 1e-4}
+    ], weight_decay=1e-6)
+    
+    scheduler = ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=7)
+
+    # train the model
+    trained_model = train_model(model, train_loader, valid_loader, criterion, optimizer, scheduler, num_epochs=50, device=device)
+
+    # eval the model
+    evaluate_model(trained_model, test_loader, device=device)
+
+    # save the model weights
+    torch.save(trained_model.state_dict(), 'lung_cancer_detection_model.pth')
+
+    # save the model in ONNX format
+    dummy_input = torch.randn(1, 3, 224, 224).to(device)
+    torch.onnx.export(trained_model, dummy_input, "lung_cancer_detection_model.onnx", input_names=['input'], output_names=['output'])
+
+    print("Training completed. Model saved.")
\ No newline at end of file
diff --git a/preprocess.py b/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff89a83361b1af2bda692eb267859a2e05240c7b
--- /dev/null
+++ b/preprocess.py
@@ -0,0 +1,31 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader
+import os
+import copy
+
+# define data transformations
+transform = transforms.Compose([
+    transforms.Resize((250, 250)),  # resize images to 250x250 pixels
+    transforms.ToTensor(),  # convert images to pytorch tensors
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # normalize images with mean and std deviation of imagenet dataset
+])
+
+# specify the directory containing the dataset
+data_dir = 'Processed_Data'
+
+# load the datasets with the specified transformations
+train_dataset = datasets.ImageFolder(os.path.join(data_dir, 'train'), transform=transform)  # load training dataset with transformations
+valid_dataset = datasets.ImageFolder(os.path.join(data_dir, 'valid'), transform=transform)  # load validation dataset with transformations
+test_dataset = datasets.ImageFolder(os.path.join(data_dir, 'test'), transform=transform)  # load test dataset with transformations
+
+# set batch size for the dataloaders
+batch_size = 32  # number of images to be processed in one iteration
+
+
+# print dataset sizes for confirmation
+print(f"Number of training images: {len(train_dataset)}") # 600
+print(f"Number of validation images: {len(valid_dataset)}") # 72
+print(f"Number of test images: {len(test_dataset)}") # 315
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2be4f562056d23e2de0829258b456631f5746e59
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,8 @@
+torch
+torchvision
+numpy
+Pillow
+scikit-learn
+onnx
+onnxruntime
+gradio
\ No newline at end of file
diff --git a/run.py b/run.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1cfcdc38844eff949390d3c4af9c1b4d8c601c8
--- /dev/null
+++ b/run.py
@@ -0,0 +1,35 @@
+import torch
+from PIL import Image
+from torchvision import transforms
+from architecture import ResNetLungCancer 
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+model = ResNetLungCancer(num_classes=4)
+model.load_state_dict(torch.load('Model/lung_cancer_detection_model.pth', map_location=device))
+model = model.to(device)
+model.eval()
+
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# image from local file
+image_path = "Data/test/large.cell.carcinoma/000108.png"
+image = Image.open(image_path).convert('RGB') 
+
+# preprocess the image
+input_tensor = preprocess(image).unsqueeze(0).to(device)  # add batch dimension and move to device
+
+# get model predictions
+with torch.no_grad():
+    output = model(input_tensor)
+
+predicted_class = torch.argmax(output, dim=1).item()
+
+class_names = ['Adenocarcinoma', 'Large Cell Carcinoma', 'Normal', 'Squamous Cell Carcinoma']
+
+print(f"Predicted class: {class_names[predicted_class]}")
\ No newline at end of file