updated

.gitignore

@@ -0,0 +1,4 @@
+venv/
+.env
+.env.*
+.venv/

app.py

@@ -0,0 +1,149 @@
+import os
+os.environ["STREAMLIT_WATCHER_TYPE"] = "none"  
+os.environ["STREAMLIT_WATCH_DISABLE"] = "true"  
+
+import streamlit as st
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+import pandas as pd
+
+from utils.preprocessing import get_transforms
+from models.resnet_model import ResNet18
+
+# Class names in order
+class_names = [
+    'calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting',
+    'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping',
+    'texting', 'using_laptop'
+]
+
+@st.cache_resource
+def load_model():
+    if not os.path.exists("models/best_model.pth"):
+        st.error("Model weights not found. Please ensure 'models/best_model.pth' exists.")
+        st.stop()
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = ResNet18(num_classes=15)
+    model.load_state_dict(torch.load("models/best_model.pth", map_location=device))
+    model.to(device)
+    model.eval()
+    return model, device
+
+def predict(image, model, device):
+    transform = get_transforms()
+    image_t = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        outputs = model(image_t)
+        probs = torch.softmax(outputs, dim=1)
+        conf, predicted = torch.max(probs, 1)
+    return class_names[predicted.item()], conf.item()
+
+def main():
+    st.title("Human Action Recognition App")
+    tab1, tab2, tab3 = st.tabs(["About", "Predict", "Metrics & Test Predictions"])
+
+    with tab1:
+        st.header("About This App")
+        st.markdown("""
+        ### 🧠 Human Action Recognition (HAR)
+
+        This application classifies **human actions** from static images using a deep learning model trained on a curated dataset of 15 different activities.
+
+        #### 🔍 Purpose
+        To demonstrate how computer vision and deep learning can be used to **recognize and classify human behaviors** in images — useful for applications such as surveillance, activity monitoring, and human-computer interaction.
+
+        #### 🧰 Model
+        - **Architecture:** ResNet18 (Residual Neural Network with 18 layers)  
+        - **Pretrained:** On ImageNet for general features  
+        - **Fine-tuned:** On a specialized Human Action Recognition dataset for task-specific learning
+
+        #### 📚 Dataset
+        - **Source:** [Bingsu/Human_Action_Recognition](https://huggingface.co/datasets/Bingsu/Human_Action_Recognition)  
+        - **Categories:** 15 action classes  
+          - `calling`, `clapping`, `cycling`, `dancing`, `drinking`, `eating`, `fighting`,  
+            `hugging`, `laughing`, `listening_to_music`, `running`, `sitting`,  
+            `sleeping`, `texting`, `using_laptop`
+
+        ---
+        🖼️ Simply upload an image, and the model will analyze and classify the dominant action being performed.
+        """)
+
+    with tab2:
+        st.header("Predict Human Action from Image")
+        uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+        if uploaded_file is not None:
+            try:
+                image = Image.open(uploaded_file).convert("RGB")
+            except Exception:
+                st.error("Error loading image. Please upload a valid JPG or PNG file.")
+                return
+
+            st.image(image, caption="Uploaded Image", use_container_width=True)
+
+            model, device = load_model()
+            prediction, confidence = predict(image, model, device)
+            pred_label = prediction.replace('_', ' ').title()
+            st.success(f"Predicted Action: **{pred_label}**")
+            st.info(f"Confidence: {confidence*100:.2f}%")
+
+            # Show transformed input
+            transform = get_transforms()
+            transformed_image = transform(image)
+            st.image(transforms.ToPILImage()(transformed_image), caption="Transformed Input (for model)", use_container_width=True)
+
+            # Detailed explanation based on predicted output
+            st.markdown(f"""
+            ### About the Transformed Input for **{pred_label}**
+
+            Before the model makes its prediction, the uploaded image undergoes several preprocessing steps to prepare it for analysis:
+
+            - **Resizing and cropping:** The image is resized and cropped to a consistent size (usually 224x224 pixels) so that the model receives uniform input dimensions.
+            - **Normalization:** Pixel color values are scaled based on mean and standard deviation values (typically from ImageNet dataset statistics). This helps the model generalize better by standardizing the input distribution.
+            - **Conversion to Tensor:** The image is converted from a PIL image to a PyTorch tensor, which is the required input format for the model.
+
+            This processed image is exactly what the model "sees" when it predicts the action **{pred_label}**. Understanding this helps ensure the model's input is consistent and reliable.
+            """)
+
+    with tab3:
+        st.header("Training & Validation Metrics")
+
+        st.markdown("""
+        **Training Accuracy (96.5%)**  
+        During training, the model correctly identified human actions in 96.5% of the images. This indicates it has effectively learned the patterns and features present in the training data.
+
+        **Validation Accuracy (96.6%)**  
+        When evaluated on new, unseen images, the model correctly classified 96.6% of them. This demonstrates its ability to generalize knowledge beyond simply memorizing the training examples.
+
+        **Training Loss (0.12)**  
+        The average prediction error during training was low (0.12), meaning the model’s guesses are generally close to the true labels.
+
+        **Validation Loss (0.10)**  
+        On unseen data, the prediction error was even lower (0.10), suggesting the model is not overfitting but genuinely learning to understand the task.
+        """)
+
+        st.markdown("---")
+        st.header("Test Set Predictions Preview")
+
+        st.markdown("""
+        The table below presents a sample of the model’s predictions on the test dataset, which consists of images the model has not encountered during training. The columns typically include:
+
+        - **Filename:** The name of the test image file  
+        - **Predicted Label:** The human action predicted by the model  
+        - **Confidence:** The model’s confidence score for each prediction  
+
+        Reviewing this information aids in evaluating the model’s real-world performance and helps identify potential failure cases.
+        """)
+
+        csv_path = "test_predictions.csv"
+        if os.path.exists(csv_path):
+            df = pd.read_csv(csv_path)
+            st.dataframe(df.head(20))  # show first 20 rows
+            st.success(f"Loaded {len(df)} test predictions.")
+        else:
+            st.warning(f"Test predictions CSV file not found at: {csv_path}")
+
+if __name__ == "__main__":
+    main()

models/best_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1597cf27a63fbc81303e5c0a519611ca5f9b314a88983ac526e9e5e84aa2d3ac
+size 44815819

models/resnet_model.py

@@ -0,0 +1,27 @@
+import torch
+import torch.nn as nn
+import torchvision.models as models
+
+class ResNet18(nn.Module):
+    def __init__(self, num_classes=15):
+        super(ResNet18, self).__init__()
+        self.model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
+        # Replace the last fully connected layer
+        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)
+
+    def forward(self, x):
+        x = self.model.conv1(x)
+        x = self.model.bn1(x)
+        x = self.model.relu(x)
+        x = self.model.maxpool(x)
+
+        x = self.model.layer1(x)
+        x = self.model.layer2(x)
+        x = self.model.layer3(x)
+        x = self.model.layer4(x)
+
+        x = self.model.avgpool(x)
+        x = torch.flatten(x, 1)   # <-- here torch.flatten is used, so torch must be imported
+        x = self.model.fc(x)
+
+        return x

requirements.txt

@@ -1,3 +1,6 @@
-altair
-pandas
-streamlit
+torch
+torchvision
+streamlit
+datasets
+tqdm
+Pillow

src/dataset.py

@@ -0,0 +1,24 @@
+import torch
+from torch.utils.data import Dataset
+
+class HumanActionDataset(Dataset):
+    def __init__(self, hf_dataset_split, transform=None):
+        """
+        hf_dataset_split: Hugging Face dataset split, e.g. ds['train']
+        transform: torchvision transforms
+        """
+        self.dataset = hf_dataset_split
+        self.transform = transform
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        item = self.dataset[idx]
+        image = item["image"]  # PIL.Image.Image
+        label = item["labels"]
+
+        if self.transform:
+            image = self.transform(image)
+
+        return image, label

src/evaluate.py

@@ -0,0 +1,97 @@
+import torch
+from torch.utils.data import DataLoader, random_split
+from datasets import load_dataset
+from src.dataset import HumanActionDataset
+from utils.preprocessing import get_transforms
+from models.resnet_model import ResNet18
+import pandas as pd
+
+def get_val_loader(batch_size=32):
+    ds = load_dataset("Bingsu/Human_Action_Recognition")
+    full_dataset = HumanActionDataset(ds['train'], transform=get_transforms())
+
+    train_size = int(0.8 * len(full_dataset))
+    val_size = len(full_dataset) - train_size
+    _, val_dataset = random_split(full_dataset, [train_size, val_size])
+
+    val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
+    return val_loader
+
+def get_test_loader(batch_size=32):
+    ds = load_dataset("Bingsu/Human_Action_Recognition")
+    test_dataset = HumanActionDataset(ds['test'], transform=get_transforms())
+    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+    return test_loader
+
+def evaluate_model(model_path, batch_size=32):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    model = ResNet18(num_classes=15)
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.to(device)
+    model.eval()
+
+    val_loader = get_val_loader(batch_size)
+
+    correct = 0
+    total = 0
+
+    with torch.no_grad():
+        for images, labels in val_loader:
+            images = images.to(device)
+            labels = labels.to(device)
+
+            outputs = model(images)
+            _, predicted = torch.max(outputs, 1)
+
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+
+    accuracy = correct / total
+    print(f"Validation Accuracy: {accuracy:.4f} ({correct}/{total})")
+
+def predict_test(model_path, batch_size=32, output_csv="test_predictions.csv"):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    model = ResNet18(num_classes=15)
+    model.load_state_dict(torch.load(model_path, map_location=device))
+    model.to(device)
+    model.eval()
+
+    test_loader = get_test_loader(batch_size)
+    class_names = [
+        'calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting',
+        'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping',
+        'texting', 'using_laptop'
+    ]
+
+    predictions = []
+    filenames = []
+
+    with torch.no_grad():
+        for batch in test_loader:
+            images = batch[0].to(device)
+            # Assuming your HumanActionDataset returns (image, label, filename) or just (image, label)
+            # If filenames are needed and your dataset doesn't provide, you can omit or modify accordingly.
+            
+            outputs = model(images)
+            _, predicted = torch.max(outputs, 1)
+
+            predictions.extend(predicted.cpu().tolist())
+            # If you have filenames:
+            # filenames.extend(batch[2])  # Uncomment if your dataset yields filenames
+
+    # If you don't have filenames, just save predictions indexed by order
+    df = pd.DataFrame({
+        "id": list(range(len(predictions))),
+        "label": [class_names[p] for p in predictions]
+    })
+    df.to_csv(output_csv, index=False)
+    print(f"Saved test predictions to {output_csv}")
+
+if __name__ == "__main__":
+    model_path = "models/best_model.pth"
+    evaluate_model(model_path=model_path, batch_size=32)
+    predict_test(model_path=model_path, batch_size=32, output_csv="test_predictions.csv")

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))

src/train.py

@@ -0,0 +1,109 @@
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, random_split
+from torchvision import transforms
+from tqdm import tqdm
+from datasets import load_dataset
+
+from utils.preprocessing import get_transforms
+from src.dataset import HumanActionDataset
+from models.resnet_model import ResNet18
+
+import os
+
+def train_model():
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    # Load dataset from Hugging Face
+    ds = load_dataset("Bingsu/Human_Action_Recognition")
+
+    # Get train dataset and apply transforms
+    transform = get_transforms()
+    full_dataset = HumanActionDataset(ds["train"], transform=transform)
+
+    # Split train into train/val (e.g., 90% train, 10% val)
+    train_size = int(0.9 * len(full_dataset))
+    val_size = len(full_dataset) - train_size
+    train_dataset, val_dataset = random_split(full_dataset, [train_size, val_size])
+
+    # Use batch size 32 (good balance between speed and generalization on CPU)
+    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
+    val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, num_workers=4)
+
+    # Initialize model
+    model = ResNet18(num_classes=15).to(device)
+
+    criterion = nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+
+    # Scheduler without verbose (fix for your PyTorch version)
+    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+        optimizer, mode='min', factor=0.5, patience=2
+    )
+
+    best_val_acc = 0.0
+    epochs = 10
+
+    for epoch in range(epochs):
+        model.train()
+        train_loss = 0.0
+        correct = 0
+        total = 0
+
+        loop = tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs} Training")
+        for images, labels in loop:
+            images, labels = images.to(device), labels.to(device)
+
+            optimizer.zero_grad()
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            train_loss += loss.item() * images.size(0)
+            _, predicted = torch.max(outputs, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+
+            loop.set_postfix(loss=loss.item(), acc=correct/total)
+
+        train_loss /= total
+        train_acc = correct / total
+
+        # Validation
+        model.eval()
+        val_loss = 0.0
+        val_correct = 0
+        val_total = 0
+
+        with torch.no_grad():
+            for images, labels in val_loader:
+                images, labels = images.to(device), labels.to(device)
+                outputs = model(images)
+                loss = criterion(outputs, labels)
+
+                val_loss += loss.item() * images.size(0)
+                _, predicted = torch.max(outputs, 1)
+                val_total += labels.size(0)
+                val_correct += (predicted == labels).sum().item()
+
+        val_loss /= val_total
+        val_acc = val_correct / val_total
+
+        print(f"Epoch {epoch+1}/{epochs} | "
+              f"Train Loss: {train_loss:.4f} | Train Acc: {train_acc:.4f} | "
+              f"Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.4f}")
+
+        # Step the scheduler with validation loss
+        scheduler.step(val_loss)
+
+        # Save best model
+        if val_acc > best_val_acc:
+            best_val_acc = val_acc
+            os.makedirs("models", exist_ok=True)
+            torch.save(model.state_dict(), "models/best_model.pth")
+            print("Saved best model.")
+
+if __name__ == "__main__":
+    train_model()

test_predictions.csv

@@ -0,0 +1,5401 @@
+id,label
+0,sleeping
+1,eating
+2,running
+3,eating
+4,texting
+5,clapping
+6,dancing
+7,hugging
+8,laughing
+9,drinking
+10,calling
+11,drinking
+12,dancing
+13,sleeping
+14,using_laptop
+15,drinking
+16,calling
+17,using_laptop
+18,hugging
+19,dancing
+20,drinking
+21,running
+22,laughing
+23,running
+24,fighting
+25,fighting
+26,hugging
+27,sitting
+28,sleeping
+29,laughing
+30,running
+31,sitting
+32,laughing
+33,eating
+34,drinking
+35,sleeping
+36,cycling
+37,clapping
+38,fighting
+39,fighting
+40,eating
+41,hugging
+42,eating
+43,eating
+44,drinking
+45,eating
+46,using_laptop
+47,dancing
+48,eating
+49,sitting
+50,using_laptop
+51,dancing
+52,drinking
+53,clapping
+54,calling
+55,clapping
+56,fighting
+57,laughing
+58,hugging
+59,using_laptop
+60,calling
+61,texting
+62,clapping
+63,listening_to_music
+64,calling
+65,running
+66,texting
+67,laughing
+68,calling
+69,running
+70,sleeping
+71,dancing
+72,using_laptop
+73,using_laptop
+74,sitting
+75,running
+76,sleeping
+77,running
+78,running
+79,dancing
+80,texting
+81,calling
+82,sleeping
+83,sleeping
+84,running
+85,drinking
+86,listening_to_music
+87,using_laptop
+88,drinking
+89,sitting
+90,running
+91,cycling
+92,sitting
+93,sleeping
+94,listening_to_music
+95,laughing
+96,sleeping
+97,listening_to_music
+98,texting
+99,running
+100,drinking
+101,running
+102,using_laptop
+103,dancing
+104,listening_to_music
+105,listening_to_music
+106,hugging
+107,dancing
+108,hugging
+109,cycling
+110,clapping
+111,hugging
+112,sleeping
+113,using_laptop
+114,cycling
+115,eating
+116,cycling
+117,cycling
+118,eating
+119,calling
+120,listening_to_music
+121,sleeping
+122,listening_to_music
+123,cycling
+124,hugging
+125,fighting
+126,sitting
+127,using_laptop
+128,running
+129,calling
+130,cycling
+131,hugging
+132,clapping
+133,calling
+134,eating
+135,laughing
+136,listening_to_music
+137,texting
+138,eating
+139,eating
+140,cycling
+141,listening_to_music
+142,calling
+143,sleeping
+144,using_laptop
+145,drinking
+146,dancing
+147,laughing
+148,sleeping
+149,drinking
+150,hugging
+151,calling
+152,sleeping
+153,hugging
+154,fighting
+155,hugging
+156,laughing
+157,sitting
+158,sleeping
+159,calling
+160,using_laptop
+161,calling
+162,laughing
+163,listening_to_music
+164,cycling
+165,clapping
+166,listening_to_music
+167,running
+168,clapping
+169,sitting
+170,dancing
+171,dancing
+172,dancing
+173,clapping
+174,dancing
+175,fighting
+176,calling
+177,calling
+178,sitting
+179,laughing
+180,sitting
+181,cycling
+182,clapping
+183,hugging
+184,eating
+185,calling
+186,sleeping
+187,sleeping
+188,listening_to_music
+189,laughing
+190,dancing
+191,sleeping
+192,running
+193,hugging
+194,laughing
+195,dancing
+196,drinking
+197,calling
+198,fighting
+199,clapping
+200,laughing
+201,listening_to_music
+202,listening_to_music
+203,eating
+204,texting
+205,drinking
+206,cycling
+207,drinking
+208,listening_to_music
+209,eating
+210,listening_to_music
+211,listening_to_music
+212,fighting
+213,running
+214,clapping
+215,drinking
+216,running
+217,drinking
+218,clapping
+219,eating
+220,using_laptop
+221,cycling
+222,fighting
+223,listening_to_music
+224,sleeping
+225,hugging
+226,texting
+227,fighting
+228,fighting
+229,running
+230,running
+231,clapping
+232,calling
+233,hugging
+234,cycling
+235,cycling
+236,cycling
+237,sleeping
+238,dancing
+239,clapping
+240,clapping
+241,dancing
+242,sleeping
+243,sleeping
+244,using_laptop
+245,clapping
+246,cycling
+247,using_laptop
+248,calling
+249,fighting
+250,sleeping
+251,using_laptop
+252,laughing
+253,sitting
+254,laughing
+255,cycling
+256,sleeping
+257,eating
+258,clapping
+259,sitting
+260,calling
+261,cycling
+262,dancing
+263,drinking
+264,calling
+265,sitting
+266,sleeping
+267,using_laptop
+268,fighting
+269,running
+270,texting
+271,running
+272,running
+273,listening_to_music
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+5237,clapping
+5238,clapping
+5239,cycling
+5240,clapping
+5241,using_laptop
+5242,laughing
+5243,drinking
+5244,fighting
+5245,laughing
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+5248,listening_to_music
+5249,sleeping
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+5272,eating
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+5279,laughing
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+5281,hugging
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+5358,texting
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+5398,sitting
+5399,dancing

utils/preprocessing.py

@@ -0,0 +1,12 @@
+from torchvision import transforms
+
+def get_transforms():
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize(
+            mean=[0.485, 0.456, 0.406],  # ImageNet means
+            std=[0.229, 0.224, 0.225]    # ImageNet stds
+        )
+    ])
+    return transform