Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+impor 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('hf')
+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
+@st.cache_resource
+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 - Image Captioning and Retrieval")
+
+# 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=3)
+
+# 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)