Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import re
-import os
 import numpy as np
 from langchain_community.vectorstores import Chroma
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
@@ -23,32 +22,34 @@ def similarity_search2(vectordb, query, k, unique="True"):
         return str(np.unique(np.array(temp)))[1:-1]
     else:
         return str(np.array(temp))[1:-1]
-
+    
 with gr.Blocks() as demo:
     gr.Markdown(
     """
     <h2> <center> Query Retrieval </center> </h2>
     """)
-    query = gr.Textbox(placeholder="your query", label="Query")
-    k = gr.Slider(10,1000,5, label="number of samples to check")
-    unique = gr.Radio(["True", "False"], label="Return Unique values")
-    
     with gr.Row():
-        btn = gr.Button("Submit")
-    
-    def mmt_query(query, k, unique):
-        model_id = "BAAI/bge-large-en-v1.5"
-        model_kwargs = {"device": "cpu"}
-        embedding = HuggingFaceBgeEmbeddings(
-            model_name = model_id,
-            model_kwargs = model_kwargs,
-            encode_kwargs = {'normalize_embeddings':True}
-        )
-        persist_directory = r"VectorDB\db_book_mmt"
-        vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
-        return similarity_search2(vectordb, query, k, unique)
-    
-    output = gr.Textbox()
-    btn.click(mmt_query, [query, k, unique], output)
+        with gr.Column():
+            query = gr.Textbox(placeholder="your query", label="Query")
+            k = gr.Slider(10,1000,5, label="number of samples to check")
+            unique = gr.Radio(["True", "False"], label="Return Unique values")
+            with gr.Row():
+                btn = gr.Button("Submit")
+            def mmt_query(query, k, unique):
+                model_id = "BAAI/bge-large-en-v1.5"
+                model_kwargs = {"device": "cpu"}
+                embedding = HuggingFaceBgeEmbeddings(
+                    model_name = model_id,
+                    model_kwargs = model_kwargs,
+                    encode_kwargs = {'normalize_embeddings':True}
+                )
+                persist_directory = r"VectorDB\db_book_mmt"
+                vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
+                return similarity_search2(vectordb, query, k, unique)
+        with gr.Column():
+            output = gr.Textbox(scale=10, label="Output")
+            btn.click(mmt_query, [query, k, unique], output)
     
+    # interface = gr.Interface(fn=auto_eda, inputs="dataframe", outputs="json")
+# demo.queue()
 demo.launch()