sunc with remote

app.py

@@ -0,0 +1,66 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import os
+from PIL import Image
+import torch.nn as nn
+import pandas as pd
+import torch
+from torch.optim import Adam
+from torch.utils.data import Dataset, DataLoader
+
+
+# Load the dataset
+st.session_state.train_df = pd.read_csv("data/asl_data/sign_mnist_train.csv")
+st.session_state.valid_df = pd.read_csv("data/asl_data/sign_mnist_valid.csv")
+
+# Streamlit app
+st.title("ASL Recognition App")
+
+# Tabs: Dataset, Model, Prediction
+tab1, tab2, tab3 = st.tabs(["Dataset", "Model", "Prediction"])
+
+# Dataset Tab
+with tab1:
+    st.header("Dataset Overview")
+    st.write("Displaying sample images from the training dataset.")
+    
+    # Convert CSV image data to images (Placeholder code for now)
+    num_samples = 10
+    train_data = st.session_state.train_df.values[:, 1:]
+    labels = st.session_state.train_df.values[:, 0]
+    
+    # Display sample images
+    col1, col2, col3, col4, col5 = st.columns(5)
+    for i in range(num_samples):
+        img = train_data[i].reshape(28, 28)  # Assuming images are 28x28 grayscale
+        img = Image.fromarray(img.astype('uint8'))
+        with [col1, col2, col3, col4, col5][i % 5]:
+            st.image(img, caption=f"Label: {labels[i]}", use_column_width=True)
+
+# Model Tab
+with tab2:
+    st.header("Model Training")
+    st.write("Click the button to train the model.")
+    if st.button("Train Model"):
+        st.write("Training started... (Placeholder for model training)")
+
+# Prediction Tab
+with tab3:
+    st.header("Make a Prediction")
+    
+    # File uploader
+    uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+    
+    # Camera input
+    camera_file = st.camera_input("Take a picture")
+    
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+        st.write("(Placeholder for image preprocessing and prediction)")
+    
+    elif camera_file is not None:
+        image = Image.open(camera_file)
+        st.image(image, caption="Captured Image", use_column_width=True)
+        st.write("(Placeholder for image preprocessing and prediction)")

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+pillow
+torch
+torchvision
+pandas