app.py

import streamlit as st
import torch
import numpy as np
from PIL import Image
import pickle
import torchvision.transforms as transforms
from huggingface_hub import hf_hub_download
from datasets import load_dataset

# Model repository ID
MODEL_REPO_ID = "louiecerv/amer_sign_lang_neuralnet"
MODEL_FILENAME = "trained_model.pkl"  # The filename of your model on Hugging Face

# Load dataset from Hugging Face
DATASET_NAME = "louiecerv/american_sign_language"  # Replace with your dataset name
dataset = load_dataset(DATASET_NAME, split="train")

def preprocess_image(image: Image) -> tuple[torch.Tensor, Image]:
    """
    Preprocess the image by converting it to grayscale, resizing it to 28x28,
    normalizing the pixel values, and converting it to a tensor.

    Args:
        image (Image): The input image.

    Returns:
        tuple[torch.Tensor, Image]: A tuple containing the preprocessed image tensor and the processed PIL image.
    """
    try:
        transform = transforms.Compose([
            transforms.Grayscale(num_output_channels=1),
            transforms.Resize((28, 28)),
            transforms.ToTensor(),
            transforms.Normalize(mean=0.5, std=0.5)
        ])
        tensor_image = transform(image)

        # Convert the tensor back to a PIL Image for display
        tensor_image_pil = tensor_image.squeeze().cpu().numpy()  # Remove batch dimension and convert to numpy
        tensor_image_pil = (tensor_image_pil * 0.5 + 0.5) * 255  # Unnormalize
        tensor_image_pil = tensor_image_pil.astype(np.uint8)  # Convert to uint8 for PIL
        processed_image_pil = Image.fromarray(tensor_image_pil)

        return tensor_image, processed_image_pil
    except Exception as e:
        st.error(f"Error preprocessing image: {e}")
        return None, None

def load_model(repo_id: str, filename: str) -> torch.nn.Module:
    """
    Load the model from Hugging Face Hub.

    Args:
        repo_id (str): The repository ID of the model.
        filename (str): The filename of the model.

    Returns:
        torch.nn.Module: The loaded model.
    """
    try:
        model_path = hf_hub_download(repo_id=repo_id, filename=filename)
        with open(model_path, "rb") as f:
            model = pickle.load(f)
        return model
    except Exception as e:
        st.error(f"Error loading model: {e}")
        return None

def make_prediction(model: torch.nn.Module, image_tensor: torch.Tensor) -> str:
    """
    Make a prediction using the loaded model and the preprocessed image tensor.

    Args:
        model (torch.nn.Module): The loaded model.
        image_tensor (torch.Tensor): The preprocessed image tensor.

    Returns:
        str: The predicted letter.
    """
    try:
        model.eval()
        with torch.no_grad():
            # Add batch dimension if not already present
            if len(image_tensor.shape) == 3:
                image_tensor = image_tensor.unsqueeze(0)
            prediction = model(image_tensor)
        predicted_class = torch.argmax(prediction).item()
        predicted_letter = chr(predicted_class + ord('A'))
        return predicted_letter
    except Exception as e:
        st.error(f"Error making prediction: {e}")
        return None

def tensor_to_image(pixel_list):
    """Converts a tensor to a displayable image."""
    array = np.array(pixel_list).reshape(28, 28)
    array = (array * 0.5 + 0.5) * 255  # Assuming mean=0.5, std=0.5
    array = np.clip(array, 0, 255).astype(np.uint8)
    return Image.fromarray(array)

# Streamlit App
st.title("American Sign Language App")

# Create tabs
tabs = ["Dataset", "Prediction"]
selected_tab = st.sidebar.radio("Select Tab", tabs)

if selected_tab == "Dataset":
    st.header("Dataset")
    st.write("Displaying the first 20 images from the dataset.")

    # Create a grid layout
    cols = 5  # Number of columns
    rows = 4  # Number of rows
    num_images = cols * rows

    # Display images in a grid
    image_list = dataset[:num_images]["pixel_values"]
    labels = dataset[:num_images]["label"]

    # Display images using Streamlit columns
    for row in range(rows):
        columns = st.columns(cols)
        for col in range(cols):
            index = row * cols + col
            image = tensor_to_image(image_list[index])
            columns[col].image(image, caption=f"Label: {chr(labels[index] + ord('A'))}", use_container_width=True)

elif selected_tab == "Prediction":
    st.header("Prediction")
    st.write("Upload an image of an ASL letter.")

    # File uploader
    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        # Load and preprocess the image
        image = Image.open(uploaded_file).convert("RGB")  # Ensure RGB for consistent processing
        st.image(image, caption="Uploaded Image.", use_container_width=True)
        image_tensor, processed_image_pil = preprocess_image(image)

        if image_tensor is not None and processed_image_pil is not None:
            st.image(processed_image_pil, caption="Preprocessed Image.", use_container_width=True)  # Display processed image

            # Load the model
            model = load_model(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME)

            if model is not None:
                # Make a prediction
                predicted_letter = make_prediction(model, image_tensor)

                if predicted_letter is not None:
                    st.write(f"Predicted Letter: {predicted_letter}")