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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() |