import streamlit as st from PIL import Image import torch from transformers import AutoModelForCausalLM, AutoTokenizer import os def load_model(): # Placeholder for model loading # In real implementation, you would load the Maya model here pass def process_image_text(image, text, language): # Placeholder for processing # In real implementation, this would use the Maya model pass def main(): st.set_page_config(page_title="Maya - Multilingual Vision Language Assistant", layout="wide") st.title("🌟 Maya: Multimodal Multilingual Assistant") st.markdown("Interact with images and text in multiple languages") # Sidebar for language selection languages = ["English", "Hindi", "Spanish", "French", "Chinese", "Arabic"] selected_language = st.sidebar.selectbox("Select Language", languages) # Main content area col1, col2 = st.columns(2) with col1: st.subheader("Upload Image") uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"]) if uploaded_file is not None: image = Image.open(uploaded_file) st.image(image, caption="Uploaded Image", use_container_width=True) with col2: st.subheader("Enter Your Query") user_query = st.text_area("Type your question about the image...") if st.button("Process"): if uploaded_file is None: st.error("Please upload an image first!") elif not user_query: st.error("Please enter a query!") else: with st.spinner("Processing..."): try: response = process_image_text(image, user_query, selected_language) st.success("Processing Complete!") st.markdown("### Response:") st.write(response) except Exception as e: st.error(f"An error occurred: {str(e)}") # Footer st.markdown("---") st.markdown("Powered by Maya: Multimodal Multilingual LLM") if __name__ == "__main__": main()