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import gradio as gr |
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import pandas as pd |
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from langchain_community.vectorstores import SKLearnVectorStore |
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from langchain_community.embeddings import HuggingFaceBgeEmbeddings |
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cols = [ |
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"Name", |
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"Description", |
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"Category", |
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"AI-Driven", |
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"Champion", |
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"Match score", |
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] |
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persist_path = "aecaihub.parquet" |
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model_name = "BAAI/bge-small-en-v1.5" |
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encode_kwargs = {'normalize_embeddings': True,"show_progress_bar":False,"batch_size":1} |
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embeddings_function = HuggingFaceBgeEmbeddings( |
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model_name=model_name, |
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encode_kwargs=encode_kwargs, |
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query_instruction="Represent this sentence for searching relevant passages: " |
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) |
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vector_store = SKLearnVectorStore( |
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embedding=embeddings_function, persist_path=persist_path, serializer="parquet" |
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) |
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def predict(query,k): |
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docs = vector_store.similarity_search_with_score(query,k = k) |
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df_results = [] |
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for doc,score in docs: |
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m = doc.metadata |
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result_doc = { |
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"Name":f"**[{m['name']}]({m['url']})**", |
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"Description":doc.page_content, |
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"Category":m["category"], |
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"AI-Driven":m["ai_driven"], |
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"Champion":m["champion"], |
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"Match score":round(1-score,3), |
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} |
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df_results.append(result_doc) |
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df_results = pd.DataFrame(df_results) |
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return df_results |
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examples = [ |
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"Tool to generate floor plans" |
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"AI tool for comparing building materials and sustainability", |
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"3D model library with image search function", |
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"AI-powered 3D design tool for architects and interior designers", |
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"Software for extracting 3D models from videos", |
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"AI tool for comprehensive utility data in infrastructure projects", |
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"AI for generating creative content in design projects", |
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"AI tool to convert text into architectural videos", |
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"AI solutions for low carbon design and data mining in architecture", |
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"Software for construction quantity estimation and progress tracking", |
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"AI interior design tool for automatic room designs" |
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] |
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with gr.Blocks() as demo: |
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gr.Markdown(""" |
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# 🏢 AEC AI Hub - Semantic Search Engine |
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This tool uses semantic search to find AI tools for Architecture Engineering and Construction (AEC) based on your question. |
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The database is drawn from the [great work](https://stjepanmikulic.notion.site/AEC-AI-Hub-b6e6eebe88094e0e9b4995da38e96768) of [Stjepan Mikulic](https://www.linkedin.com/in/stjepanmikulic/) |
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""") |
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with gr.Row(): |
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search_bar = gr.Textbox(label="Ask you question here",scale = 2) |
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k = gr.Slider(minimum=1, maximum=20, value=5, label="Number of results", step=1,interactive=True) |
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examples = gr.Examples( |
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examples,search_bar, label="Examples", |
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) |
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button = gr.Button("🔍 Search") |
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gr.Markdown("## AI Tools") |
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result_df = gr.Dataframe( |
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headers=cols, |
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wrap=True, |
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datatype=["markdown","str","str","str","str","str"], |
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column_widths = ["10%","50%","10%","10%","10%","10%"], |
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) |
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(button |
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.click(predict, inputs = [search_bar,k], outputs=[result_df]) |
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) |
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demo.launch() |
