run.py

import streamlit as st

# this caches the output to store the output and not call this function again
# and again preventing time wastage. `allow_output_mutation = True` tells the
# function to not hash the output of this function and we can get away with it
# because no arguments are passed through this.
# https://docs.streamlit.io/en/stable/api.html#streamlit.cache
@st.cache(allow_output_mutation=True, show_spinner=False)
def get_cross_modal_search_models():
  from clip.clip import CLIP
  return CLIP()

# load all the models before the app starts
with st.spinner('Loading Model with Vocabulary ... (might take sometime)'):
  model = get_cross_modal_search_models()

st.write(f'''
# Image Searching App

Find images using text and yes, there's an easter egg.
''')

app_mode = st.sidebar.selectbox(
  'Please select tasks',
  ["Text Search", "Image Search", "Text to Text Similarity"]
)

st.write('''Upload more images to cache, if you want to add more!''')
images = st.file_uploader("Images", accept_multiple_files=True, type=['png', 'jpg', 'jpeg'])

if st.button("Upload") and len(images):
  out = model.upload_images(images)
  st.write(out)
  st.write(f'''{model.n_images}''')

# slider to select the number of images to display
n_images = st.slider('Number of images to see', min_value=1, max_value = model.n_images)

if app_mode == "Image Search":
  st.write('''### Image Search''')
  st.write(f"Upload any image for similarity search. Searching {n_images} images!")
  image = st.file_uploader("Images", accept_multiple_files=False, type=['png', 'jpg', 'jpeg'])
  if st.button("Process") and image:
    out = model.visual_search(image, n_images)
    for x in out:
      st.image(x)

elif app_mode == "Text Search":
  st.write('''### Text Search''')
  text = st.text_input(f"Add the text to search. Searching {n_images} images!")
  if st.button("Process") and text:
    out = model.text_search(text, n_images)
    for x in out:
      st.image(x)

elif app_mode == "Text to Text Similarity":
  st.write('''### Text to Text Similarity
  
This requires two different inputs, first is the memory against which to check
the second input query.''')

  default_ = '''How can I sample from the EMNIST letters dataset?
Simple, efficient way to create Dataset?
How to use multiple models to perform inference on same data in parallel?
Get target list from Dataset
Sparse dataset and dataloader
Element-Wise Max Between Two Tensors?'''
  memory = st.text_area("Memory", value=default_)
  query = st.text_input("Query", value="Can I run mulitple models in parallel?")
  matches = model.text_to_text_similarity(memory.split("\n"), query)

  if st.button("Process"):
    st.write("**Query**: " + query)
    st.write("\n".join([f"- {m}" for m in matches]))