Added a CLI

README.md

@@ -5,9 +5,9 @@ This project is meant as a way to gradually bring improvements on the bayesian n
 ## Table of Contents
 
 * [Objectives 🎯](#objectives)
+* [Requirements 📋](#requirements)
 * [Running the project 🚀](#running-the-project)
 * [Development 🔨](#development)
-    * [Requirements 📋](#requirements)
 * [Citations 📝](#citations)
 
 ## Objectives 🎯
@@ -16,6 +16,12 @@ This project is meant as a way to gradually bring improvements on the bayesian n
 - [ ] Generate some graphs to visualize the data
 - [ ] Make a CLI
 
+## Requirements 📋
+
+To run the projet you need the following requirements:
+- Python 3.12
+- venv
+
 ## Running the project 🚀
 
 ```sh
@@ -29,6 +35,4 @@ python main.py
 
 ## Development 🔨
 
-### Requirements 📋
-
 ## Citations 📝

main.py

@@ -1,46 +1,54 @@
+import inquirer
+import typer
+import pyfiglet
+from yaspin import yaspin
+from train import train_model
+from predict import make_predictions
 import os
-import cv2
-import torch
-import numpy as np
 
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+choice = ""
 
-model = torch.load("bayes_cat_dog_classifier.pth")
-model.eval()
-model.to(DEVICE)
+def main():
+    choice = inquirer.list_input("What would you like to do?", choices=["Run tests", "Train a model", "Visualize training data"])
     
-IMG_SIZE = 128
+    if choice == "Run tests":
+        predictions()
+    elif choice == "Train a model":
+        training()
+    else:
+        visualize()
 
-def predict_image(image_path):
-    img = cv2.imread(image_path)
-    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) / 255.0
-    img = np.transpose(img, (2, 0, 1))  # Convert to (C, H, W)
-    img_tensor = torch.tensor(img, dtype=torch.float32).unsqueeze(0).to(DEVICE)  # Add batch dimension
+def predictions():
+    default_cats_path = "dataset/test_set/cats/"
+    default_dogs_path = "dataset/test_set/dogs/"
+    models_base_path = "models/"
 
-    model.eval()  # Set model to evaluation mode
-    with torch.no_grad():
-        output = model(img_tensor)
-        predicted = torch.argmax(output, dim=1).item()
+    model_name = inquirer.list_input("Select the model to use", choices=os.listdir(models_base_path))
+    model_path = os.path.join(models_base_path, model_name)
 
-    return "Dog" if predicted == 1 else "Cat"
+    dataset = inquirer.list_input("Select the testing data (default dataset)", choices=['Cats', 'Dogs'])
+    
+    if dataset == "Cats":
+        with yaspin(text="Making predictions...", color="cyan") as sp:
+            make_predictions(model_path, default_cats_path, "Cat", sp)
+            sp.ok("DONE")
+    else: 
+        with yaspin(text="Making predictions...", color="cyan") as sp:
+            make_predictions(model_path, default_dogs_path, "Dog", sp)
+            sp.ok("DONE")
+
+def training():
+    text = inquirer.text(message="Enter the name of the new model")
+    with yaspin(text="Training new model...", color="cyan") as sp:
+        train_model(text, sp)
+        sp.ok("DONE")
 
     
-# Cats
-preds = []
-for filename in os.listdir("dataset/test_set/cats/"):
-    img_path = os.path.join("dataset/test_set/cats/", filename)
-    prediction = predict_image(img_path)
-    preds.append(prediction)
+def visualize():
+    print("Not available yet...\n")
+    main()
 
-print(preds.count("Cat"))
-print(preds.count("Cat") / 1000)
-
-# Dogs
-preds = []
-for filename in os.listdir("dataset/test_set/dogs/"):
-    img_path = os.path.join("dataset/test_set/dogs/", filename)
-    prediction = predict_image(img_path)
-    preds.append(prediction)
-
-print(preds.count("Dog"))
-print(preds.count("Dog") / 1000)
+if __name__ == "__main__":
+    print(pyfiglet.figlet_format("Cats and Dogs"))
+    print(pyfiglet.figlet_format("classification"))
+    typer.run(main)

predict.py

@@ -0,0 +1,39 @@
+import os
+import cv2
+import torch
+import numpy as np
+
+IMG_SIZE = 128
+
+if torch.cuda.is_available():
+    DEVICE = torch.device("cuda")
+elif torch.mps.is_available():
+    DEVICE = torch.device("mps")
+else:
+    DEIVCE = torch.device("cpu")
+
+def predict_image(image_path, model):
+    img = cv2.imread(image_path)
+    img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) / 255.0
+    img = np.transpose(img, (2, 0, 1))  # Convert to (C, H, W)
+    img_tensor = torch.tensor(img, dtype=torch.float32).unsqueeze(0).to(DEVICE)  # Add batch dimension
+
+    model.eval()
+    with torch.no_grad():
+        output = model(img_tensor)
+        predicted = torch.argmax(output, dim=1).item()
+
+    return "Dog" if predicted == 1 else "Cat"
+
+def make_predictions(model_path, dataset_path, type, spinner):
+    model = torch.load(model_path, map_location=DEVICE)
+    model.eval()
+    model.to(DEVICE)
+
+    preds = []
+    for filename in os.listdir(dataset_path):
+        img_path = os.path.join(dataset_path, filename)
+        prediction = predict_image(img_path, model)
+        preds.append(prediction)
+
+    spinner.write(f'Precision : {preds.count(type) / 1000 * 100}%')

requirements.txt

@@ -1,5 +1,9 @@
+inquirer==3.4.0
 matplotlib==3.10.0
 numpy==2.2.2
 opencv-python==4.11.0.86
+pyfiglet==1.0.2
 torch==2.5.1
 torchvision==0.20.1
+typer==0.15.1
+yaspin==3.1.0

train.py

@@ -6,16 +6,17 @@ from torch.utils.data import DataLoader, TensorDataset
 from torch import nn, optim
 from BN import CatDogClassifier
 import time
+from yaspin import yaspin
 
 IMG_SIZE = 128
 
-#DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-DEVICE = "mps"
+if torch.cuda.is_available():
+    DEVICE = torch.device("cuda")
+elif torch.mps.is_available():
+    DEVICE = torch.device("mps")
+else:
+    DEIVCE = torch.device("cpu")
 
-
-"""
-This function loads all the images from the folder and labels them
-"""
 def load_images_from_folder(folder, label):
     data = []
     for filename in os.listdir(folder):
@@ -27,7 +28,9 @@ def load_images_from_folder(folder, label):
             data.append((img, label))
     return data
 
-if __name__ == "__main__":
+def train_model(model_name, spinner):
+
+    spinner.write(f'Using the following device : {DEVICE}')
 
     # Loading the dataset
     cat_data = load_images_from_folder("dataset/training_set/cats", label=0)
@@ -66,8 +69,8 @@ if __name__ == "__main__":
             optimizer.step()
             total_loss += loss.item()
 
-        print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss/len(dataloader):.4f}")
+        spinner.write(f"Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss/len(dataloader):.4f}")
 
-    print(f"Time taken: {(time.time() - start_time):.2f} seconds")
+    spinner.write(f"Time taken: {(time.time() - start_time):.2f} seconds")
 
-    torch.save(model, f"bayes_cat_dog_classifier.pth")
+    torch.save(model, f"models/{model_name}.pth")