add main files

api.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+from utils.similarity import get_similar_items
+import pandas as pd
+import numpy as np
+
+clothing_data = pd.read_csv('data/clothing_data_preprocessed.csv')
+model = SentenceTransformer('model')
+embeddings = np.load('data/embeddings.npy')
+
+app = FastAPI()
+
+class Query(BaseModel):
+    query: str
+
+@app.post("/predict")
+def getURL(query: Query):
+    # Get the query from the request payload
+    query_text = query.query
+    # Call your function to retrieve similar item URLs
+    similar_urls = get_similar_items(query_text, embeddings, clothing_data, 5)
+    return {"similar_urls": similar_urls}
+
+if __name__ == '__main__':
+    import uvicorn
+    uvicorn.run(app, host='0.0.0.0', port=8080)

main.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+from utils.similarity import get_similar_items
+import numpy as np
+import pandas as pd
+import markdown
+import random
+
+# Create title, description, and article strings
+title = "Clothing Similarity Search 👕"
+description = "**Transformer-based search engine** to fetch Amazon URLs for similar clothing items given a text description.\n\n**Data Collection**:\nTo scrape quality clothing data containing proper description and URL for the product, Apify's Amazon Product Scraper was used. The scraped data for various clothing categories was downloaded into a CSV file.\n\n**Data Cleaning**:\nPandas was used to clean and preprocess the text data by removing special characters, lowercasing, and applying text normalization techniques.\n\n**Making Embeddings**:\nSentence-transformers library was used to generate embeddings for the cleaned data using the [all-MiniLM-L6-v2](https://example.com/model-card) model. The embeddings were saved into a .npy file for faster similarity search retrieval.\n\n**Cosine Similarity**:\nCosine similarity was used to find the similarity between the query and the product embeddings.\n"
+
+model = SentenceTransformer('model')
+embeddings = np.load('data/embeddings.npy')
+clothing_data = pd.read_csv('data/clothing_data_preprocessed.csv')
+
+def getURL(text, top_k):
+    # Call your function to retrieve similar item URLs
+    similar_urls = get_similar_items(text, embeddings, clothing_data, top_k)
+    return similar_urls
+
+input_text = gr.components.Textbox(lines=1, label="Input Descriptiont")
+input_top_k = gr.components.Slider(label="Number of Recommendations", minimum=1, maximum=10, step=1, default=5)
+output_html = gr.outputs.HTML(label="Similar Items")
+
+def process_text(text, top_k):
+    urls = getURL(text, top_k)
+    random.shuffle(urls)  # Shuffle the URLs for variety
+    html_links = "<br>".join([f'<a href="{url}" target="_blank">{url}</a>' for url in urls])
+    return f'<div style="padding: 20px">{html_links}</div>'
+
+iface = gr.Interface(
+    fn=process_text,
+    inputs=[input_text, input_top_k],
+    outputs=output_html,
+    title=title,
+    description=description,
+    examples=[
+        ["casual men's t-shirt", 3],
+        ["stylish summer dress", 5],
+        ["elegant evening gown", 7],
+    ],
+    theme="default",
+    layout="vertical",
+    interpretation="default",
+    allow_flagging="never",
+)
+
+iface.launch()