Create app.py

app.py

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+import gradio as gr
+from langchain.vectorstores import FAISS
+import os
+
+os.environ["OPENAI_API_KEY"] = os.environ["openai"]
+embeddings = OpenAIEmbeddings(model="text-embedding-3-large")  # Using OpenAI's embeddings to represent text
+
+# Load the vector store
+vector_store = FAISS.load_local(
+    "yc_index", embeddings, allow_dangerous_deserialization=True
+)
+
+# Create a retriever with the vector store
+retriever = vector_store.as_retriever(search_type="mmr")
+
+# Function to use the retriever on an input query
+def retrieve_result(query, k=10):
+    retriever.search_kwargs["k"] = k
+    result = retriever.invoke(query)
+    res = []
+    for r in result:
+        formatted_result = f"""
+        <b>Name</b>: {r.metadata.get('name')}<br>
+        <b>One Liner</b>: {r.metadata.get('oneLiner')}<br>
+        <b>Website</b>: <a href='{r.metadata.get('website')}' target='_blank'>{r.metadata.get('website')}</a><br>
+        <b>Status</b>: {r.metadata.get('status')}<br>
+        <b>Locations</b>: {r.metadata.get('locations')}
+        """
+        res.append(formatted_result.strip())
+    return "<br><br>".join(res)
+
+# Set up the Gradio UI using Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("# YCombinator Startups Semantic Search")
+    #gr.Markdown("Enter a query to search the vector store for relevant results about legal tech startups.")
+    with gr.Row():
+        input_text = gr.Textbox(label="Describe your startup idea")
+    k_value = gr.Number(label="Top K startups", value=5)
+    submit_button = gr.Button("Submit")
+    with gr.Row():
+        output_text = gr.HTML(label="Result")
+
+    submit_button.click(fn=lambda query, k: '', inputs=[input_text, k_value], outputs=output_text)
+    submit_button.click(fn=retrieve_result, inputs=[input_text, k_value], outputs=output_text)   
+
+# Launch the UI
+demo.launch()