Upload gr.py

gr.py

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+import gradio as gr
+import re
+import os
+import numpy as np
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+# from dotenv import load_dotenv, find_dotenv
+
+# load_dotenv(find_dotenv(r"LLMs\.env"))
+HUGGINGFACEHUB_API_TOKEN = os.environ["token"]
+
+def clean_(l):
+    s = list(l)[0][1]
+    s = s.replace("\n", "=")
+    return re.split('=', s, maxsplit=1)[-1].strip()
+
+def similarity_search2(vectordb, query, k, unique="True"):
+    print(f"\nQuery Key: {query}, \nrows requested:{k}\nUnique values:{unique}")
+    D = vectordb.similarity_search(query,k)
+    temp = []
+    for d in D:
+        temp.append(clean_(d))
+    del D
+    if unique == "True":
+        return str(np.unique(np.array(temp)))[1:-1]
+    else:
+        return str(np.array(temp))[1:-1]
+
+with gr.Blocks() as demo:
+    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"data\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)
+    
+    # interface = gr.Interface(fn=auto_eda, inputs="dataframe", outputs="json")
+# demo.queue()
+demo.launch()