Update app.py

app.py

@@ -7,9 +7,9 @@ import torch
 import cv2
 import time
 
-# Load the publicly available Hugging Face model
-processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
-model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224-in21k")
+# Load the open-source Hugging Face sign language model
+processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
+model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")
 
 # Function for sign classification
 def classify_sign(image):
@@ -17,13 +17,23 @@ def classify_sign(image):
     inputs = processor(images=image, return_tensors="pt")
     outputs = model(**inputs)
     prediction = torch.argmax(outputs.logits, dim=-1).item()
-    labels = ["Hello", "Thank You", "Yes", "No", "Please"]  # Update with the actual model labels
+    labels = ["Hello", "Thank You", "Yes", "No", "Please"]  # Update with actual model labels if available
     return labels[prediction % len(labels)]
 
 # Streamlit UI
 def main():
+    st.set_page_config(page_title="Sign Language Translator", layout="wide")
     st.title("Sign Language Translator")
 
+    # Sidebar
+    with st.sidebar:
+        st.header("Menu")
+        st.info("Use the tabs to navigate between features.")
+        st.markdown("- 📸 **Image Load**: Upload an image to detect signs")
+        st.markdown("- 📷 **Take Picture**: Capture a sign using your camera")
+        st.markdown("- 📹 **Live**: Detect signs in real-time")
+        st.markdown("- 📝 **Text2Sign**: Convert text into sign language video")
+
     tab1, tab2, tab3, tab4 = st.tabs(["Image Load", "Take Picture", "Live", "Text2Sign"])
 
     with tab1: