Renamed main to app, since huggingface spaces only allows app as the entry script name.

src/app.py

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