Amith Adiraju
Copied entire codebase from working streamlit application from personal git. This application lets users upload images of item menus ( only supports indian item menus for now ); using opencv and llms, this application will explain each item in the menu in specific format.
9a0f501
| import streamlit as st | |
| from inference.translate import ( | |
| extract_filter_img, | |
| transcribe_menu_model, | |
| load_models | |
| ) | |
| from inference.config import DEBUG_MODE | |
| from PIL import Image | |
| import time | |
| # Streamlit app | |
| st.title("Image Upload and Processing") | |
| # Using open source text detector, LLM for explaining items | |
| text_extractor, \ | |
| item_tokenizer,item_summarizer = load_models(item_summarizer = "google/flan-t5-large") | |
| # Streamlit function to upload an image from any device | |
| uploaded_file = st.file_uploader("Choose an image...", | |
| type=["jpg", "jpeg", "png"]) | |
| # Submit button | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| # Only show if user wants to see | |
| if st.checkbox('Show Uploaded Image'): | |
| st.image(image, | |
| caption='Uploaded Image', | |
| use_column_width=True) | |
| # Submit button | |
| if st.button("Submit"): | |
| msg1 = st.empty() | |
| msg1.write("Pre-processing and extracting text out of your image ....") | |
| st_filter = time.perf_counter() | |
| # Call the extract_filter_img function | |
| filtered_text = extract_filter_img(image, text_extractor) | |
| en_filter = time.perf_counter() | |
| msg2 = st.empty() | |
| msg2.write("All pre-processing done, transcribing your menu items now ....") | |
| st_trans_llm = time.perf_counter() | |
| translated_text_dict = transcribe_menu_model(menu_texts=filtered_text, | |
| text_tokenizer=item_tokenizer, | |
| text_summarizer=item_summarizer | |
| ) | |
| msg3 = st.empty() | |
| msg3.write("Done transcribing ... ") | |
| en_trans_llm = time.perf_counter() | |
| msg1.empty(); msg2.empty(); msg3.empty() | |
| st.success("Image processed successfully! " ) | |
| if DEBUG_MODE: | |
| filter_time_sec = en_filter - st_filter | |
| llm_time_sec = en_trans_llm - st_trans_llm | |
| total_time_sec = filter_time_sec + llm_time_sec | |
| st.write("Time took to extract and filter text {}".format(filter_time_sec)) | |
| st.write("Time took to summarize by LLM {}".format(llm_time_sec)) | |
| st.write('Overall time taken in seconds: {}'.format(total_time_sec)) | |
| st.table(translated_text_dict) | |