initial commit

app.py

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+# Initialize a retriever using Qdrant and SentenceTransformer embeddings
+from langchain.vectorstores import Qdrant
+from langchain.retrievers import EnsembleRetriever
+from langchain.embeddings import SentenceTransformerEmbeddings
+from qdrant_client import QdrantClient
+import pandas as pd
+import gradio as gr
+
+
+embeddings = SentenceTransformerEmbeddings(model_name='sentence-transformers/clip-ViT-B-32')
+
+def get_results(search_results):
+    filtered_img_ids = [doc.metadata.get("image_id") for doc in search_results]
+    return filtered_img_ids
+
+vector_db_key = user_secrets.get_secret("vector_db_key")
+
+client = QdrantClient(
+    url="https://763bc1da-0673-4535-91ac-b5538ec0287f.us-east4-0.gcp.cloud.qdrant.io:6333", 
+    api_key='UOqiBgqhhu8BBWP98mwjGl7h4IhL2vMAqzO4EI9PEB66A50n9GoIiQ',
+) # Persists changes to disk, fast prototyping
+
+COLLECTION_NAME="semantic_image_search"
+
+
+dense_vector_retriever = Qdrant(client, COLLECTION_NAME, embeddings)
+images_data = pd.read_csv("/kaggle/input/fashion-product-images-dataset/fashion-dataset/images.csv", on_bad_lines='skip')
+
+def get_link(query):
+    Search_Query = query
+    neutral_retiever = EnsembleRetriever(retrievers=[dense_vector_retriever.as_retriever()])
+    result = neutral_retiever.get_relevant_documents(Search_Query)
+    filtered_images = get_results(result)
+    filtered_img_ids = [doc.metadata.get("image_id") for doc in result]
+    
+    links = [images_data.loc[id, 'link'] for id in filtered_img_ids]
+#     final = '[' + ','.join(links) + ']'
+    return links
+
+# print(get_link("black shirt for men"))
+gr.Interface(fn = get_link, inputs = 'textbox', outputs = 'textbox').launch()

requirements.txt

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+qdrant-client
+langchain
+sentence_transformers
+gradio