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main.py

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+import streamlit as st
+import random
+import os
+import clip
+from funcs.get_similarity import get_similarity_score, create_filelist, load_embeddings, find_matches
+from funcs.fiass_similaruty import load_embeddings, encode_text, find_matches_fiass
+import torch.nn.functional as F
+import pandas as pd
+
+device = 'cpu'
+model_path = "weights/ViT-B-32.pt"
+
+model, preprocess = clip.load('ViT-B/32', device)
+
+file_name = create_filelist('img')
+features = load_embeddings('embeddings/emb_images_5000.npy')
+df = pd.read_csv('data/results.csv')
+
+random_queries = ['friends playing cards', 'rock band playing on guitars', 'policeman cross the road', 
+                  'sleeping kids', 'football team playing on the grass' , 'learning programming'
+                  ]
+
+st.header('Find my pic!')
+
+request = st.text_input('Write a description of the picture', ' Two people at the photo')
+
+img_count = st.slider('How much pic you need?', 4, 8, 6, 2)
+
+
+matches = find_matches_fiass(features, request, file_name, img_count)
+row1, row2 = st.columns(2)
+    
+if st.button('Find!'):
+
+    selected_filenames = matches
+
+    for i in range(int(img_count/2)):
+        filename = selected_filenames[i]
+        img_path = filename
+        img_discription = df[df['image_name'] == filename.split('/')[1]]['comment'].iloc[0]
+        with row1:
+            st.image(img_path, width=300, caption=img_discription)
+
+    # display next 3 images in the second row
+    for i in range(int(img_count/2), img_count):
+        filename = selected_filenames[i]
+        img_path = filename
+        img_discription = df[df['image_name'] == filename.split('/')[1]]['comment'].iloc[0]
+        with row2:
+            st.image(img_path, width=300, caption=img_discription)

requirements.txt

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+clip @ git+https://github.com/openai/CLIP.git@a9b1bf5920416aaeaec965c25dd9e8f98c864f16
+clip-by-openai==1.1
+altair==4.2.2
+asttokens==2.2.1
+attrs==22.2.0
+backcall==0.2.0
+beautifulsoup4==4.12.0
+blinker==1.5
+cachetools==5.3.0
+certifi==2022.12.7
+charset-normalizer==3.1.0
+click==8.1.3
+clip==1.0
+colorama==0.4.6
+comm==0.1.3
+contourpy==1.0.7
+cycler==0.11.0
+debugpy==1.6.6
+decorator==5.1.1
+entrypoints==0.4
+executing==1.2.0
+faiss-cpu==1.7.3
+filelock==3.10.0
+fonttools==4.39.2
+ftfy==6.1.1
+gdown==4.6.4
+gitdb==4.0.10
+GitPython==3.1.31
+idna==3.4
+importlib-metadata==6.1.0
+ipykernel==6.22.0
+ipython==8.11.0
+jedi==0.18.2
+Jinja2==3.1.2
+jsonschema==4.17.3
+jupyter_client==8.1.0
+jupyter_core==5.3.0
+kiwisolver==1.4.4
+markdown-it-py==2.2.0
+MarkupSafe==2.1.2
+matplotlib==3.7.1
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+mpmath==1.3.0
+nest-asyncio==1.5.6
+networkx==3.0
+numpy==1.24.2
+opencv-python==4.7.0.72
+packaging==23.0
+pandas==1.5.3
+parso==0.8.3
+pickleshare==0.7.5
+Pillow==9.4.0
+platformdirs==3.1.1
+prompt-toolkit==3.0.38
+protobuf==3.20.3
+psutil==5.9.4
+pure-eval==0.2.2
+pyarrow==11.0.0
+pydeck==0.8.0
+Pygments==2.14.0
+Pympler==1.0.1
+pyparsing==3.0.9
+pyrsistent==0.19.3
+PySocks==1.7.1
+python-dateutil==2.8.2
+pytz==2022.7.1
+pytz-deprecation-shim==0.1.0.post0
+pyzmq==25.0.2
+regex==2023.3.23
+requests==2.28.2
+rich==13.3.2
+semver==2.13.0
+six==1.16.0
+smmap==5.0.0
+soupsieve==2.4
+stack-data==0.6.2
+streamlit==1.20.0
+sympy==1.11.1
+toml==0.10.2
+toolz==0.12.0
+torch==1.7.1
+torchvision==0.8.2
+tornado==6.2
+tqdm==4.65.0
+traitlets==5.9.0
+typing_extensions==4.5.0
+tzdata==2022.7
+tzlocal==4.3
+urllib3==1.26.15
+validators==0.20.0
+watchdog==3.0.0
+wcwidth==0.2.6
+zipp==3.15.0