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| import streamlit as st | |
| import requests | |
| import os | |
| from PIL import Image | |
| import torch | |
| import cv2 | |
| import time | |
| import numpy as np | |
| from transformers import AutoImageProcessor, AutoModelForImageClassification | |
| # Set page config as the first command | |
| st.set_page_config(page_title="Sign Language Translator", layout="wide") | |
| # Load the ASL alphabet model from Hugging Face | |
| def load_asl_model(): | |
| processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224") | |
| model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224") | |
| return processor, model | |
| processor, model = load_asl_model() | |
| # Function for ASL classification | |
| def classify_asl(image): | |
| image = image.convert("RGB") | |
| inputs = processor(images=image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| prediction = torch.argmax(outputs.logits, dim=-1).item() | |
| labels = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ") # ASL alphabet labels | |
| return labels[prediction % len(labels)] | |
| # Streamlit UI | |
| def main(): | |
| st.title("Sign Language Translator") | |
| # Sidebar | |
| with st.sidebar: | |
| st.header("Menu") | |
| col1 = st.columns(1)[0] | |
| with col1: | |
| if st.button("📖 About Us", use_container_width=True): | |
| st.write( | |
| "Welcome to the Sign Language Translator! Our platform uses advanced AI to help translate American Sign Language (ASL) alphabet gestures in real-time. " | |
| "This project aims to bridge communication gaps and make sign language more accessible for everyone." | |
| ) | |
| if st.button("📞 Contact Us", use_container_width=True): | |
| st.write( | |
| "**Phone:** +1 (555) 987-6543 " | |
| "**LinkedIn:** [Sign Language Translator](https://linkedin.com) " | |
| "**Facebook:** [Sign Language Translator](https://facebook.com) " | |
| "**Instagram:** [@signlanguage.ai](https://instagram.com) " | |
| "**Email:** contact@signlanguage.ai" | |
| ) | |
| if st.button("💬 Feedback", use_container_width=True): | |
| st.text_area("We value your feedback! Please share your thoughts below:") | |
| tab1, tab2, tab3 = st.tabs(["Image Load", "Take Picture", "Live ASL"]) | |
| with tab1: | |
| st.subheader("📸 Image Load") | |
| uploaded_image = st.file_uploader("Upload an image of an ASL alphabet gesture", type=["png", "jpg", "jpeg"]) | |
| if uploaded_image: | |
| image = Image.open(uploaded_image) | |
| st.image(image, caption="Uploaded Image", use_container_width=True) | |
| gesture = classify_asl(image) | |
| st.success(f"Detected Gesture: {gesture}") | |
| with tab2: | |
| st.subheader("📷 Take Picture") | |
| camera_image = st.camera_input("Take a picture") | |
| if camera_image: | |
| image = Image.open(camera_image) | |
| st.image(image, caption="Captured Image", use_container_width=True) | |
| gesture = classify_asl(image) | |
| st.success(f"Detected Gesture: {gesture}") | |
| with tab3: | |
| st.subheader("📹 Live ASL") | |
| if st.button("Enable Cam"): | |
| cap = cv2.VideoCapture(0) | |
| stframe = st.image([]) | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) | |
| gesture = classify_asl(image) | |
| frame = cv2.putText(frame, f"Gesture: {gesture}", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) | |
| stframe.image(frame, channels="BGR", use_container_width=True) | |
| time.sleep(1) | |
| cap.release() | |
| # Ensure proper indentation | |
| if __name__ == "__main__": | |
| main() |