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import streamlit as st | |
from PIL import Image | |
import os | |
import conch | |
from conch.open_clip_custom import create_model_from_pretrained, get_tokenizer, tokenize | |
import torch | |
from huggingface_hub import login | |
hf=os.getenv('hfk') | |
login(hf) | |
# Load the pretrained model and transforms | |
#model = create_model("hf-hub:MahmoodLab/UNI", pretrained=True, init_values=1e-5, dynamic_img_size=True) | |
# Load the model | |
def load_model(): | |
model, preprocess = create_model_from_pretrained('conch_ViT-B-16', "hf_hub:MahmoodLab/conch",hf_auth_token=hf) | |
return model, preprocess | |
model, preprocess = load_model() | |
st.title("CONCH - kurshaad's wporking space") | |
# Upload an image | |
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"]) | |
if uploaded_file: | |
# Display uploaded image | |
image = Image.open(uploaded_file) | |
st.image(image, caption='Uploaded Image', use_column_width=True) | |
# Preprocess and get image embeddings | |
image = preprocess(image).unsqueeze(0) | |
with torch.no_grad(): | |
image_embs = model.encode_image(image, proj_contrast=True, normalize=True) | |
st.write("Image embeddings generated successfully.") | |
# Text retrieval example | |
# Number of inputs to display (can be dynamic) | |
num_inputs = st.number_input("How many text inputs?", min_value=1, max_value=10, value=1) | |
# List to store user inputs | |
input_list = [] | |
# Dynamically generate the text input fields | |
for i in range(num_inputs): | |
user_input = st.text_input(f"Input Text {i+1}") | |
input_list.append(user_input) | |
populated_status = ["Populated" if text.strip() else "Empty" for text in input_list] | |
if "Populated" in populated_status: | |
# Tokenize the text | |
tokenizer = get_tokenizer() # load tokenizer | |
text_tokens = tokenize(texts=input_list, tokenizer=tokenizer) # tokenize the text | |
text_embs = model.encode_text(text_tokens) | |
#with torch.no_grad(): | |
# text_embs = model.encode_text(tokens, proj_contrast=True, normalize=True) | |
st.write("Text embeddings generated successfully.") | |
# Perform similarity check | |
similarity = torch.cosine_similarity(image_embs, text_embs) | |
st.write("Similarity check completed.") | |
st.write(similarity) |