Changed folder structure to align with hugging face streamlit spaces, where it has to be flat.

src/main.py

@@ -1,71 +0,0 @@
-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)
-        

src/requirements.txt

@@ -1,11 +0,0 @@
-sentencepiece==0.2.0
-transformers==4.44.2
-streamlit==1.37.1
-pandas==2.2.2
-altair
-easyocr==1.6.2
-matplotlib==3.7.1
-numpy==1.24.2
-Pillow==9.5.0
-nltk==3.9.1
-torch==2.1.0