Update app.py

app.py

@@ -9,21 +9,21 @@ from gradio.themes.base import Base
 #import key_param
 import os
 
-#mongo_uri = os.getenv("MONGO_URI")
-#openai_api_key = os.getenv("OPENAI_API_KEY")
+def query_data(query):
+    #mongo_uri = os.getenv("MONGO_URI")
+    #openai_api_key = os.getenv("OPENAI_API_KEY")
 
-client = MongoClient(mongo_uri)
-dbName = "langchain_demo"
-collectionName = "collection_of_text_blobs"
-collection = client[dbName][collectionName]
+    client = MongoClient(mongo_uri)
+    dbName = "langchain_demo"
+    collectionName = "collection_of_text_blobs"
+    collection = client[dbName][collectionName]
 
-# Define the text embedding model
-embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
+    # Define the text embedding model
+    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
 
-# Initialize the Vector Store
-vectorStore = MongoDBAtlasVectorSearch( collection, embeddings, index_name="default" )
+    # Initialize the Vector Store
+    vectorStore = MongoDBAtlasVectorSearch( collection, embeddings, index_name="default" )
 
-def query_data(query):
     # Convert question to vector using OpenAI embeddings
     # Perform Atlas Vector Search using Langchain's vectorStore
     # similarity_search returns MongoDB documents most similar to the query    
@@ -65,6 +65,7 @@ with gr.Blocks(theme=Base(), title="Question Answering App using Vector Search +
         # Question Answering App using Atlas Vector Search + RAG Architecture
         """)
     openai_api_key = gr.Textbox(label = "OpenAI 3.5 API Key", value = "sk-", lines = 1)
+    mongo_uri = gr.Textbox(label = "Mongo URI", value = "mongodb+srv://", lines = 1)
     textbox = gr.Textbox(label="Enter your Question:")
     with gr.Row():
         button = gr.Button("Submit", variant="primary")
@@ -74,6 +75,9 @@ with gr.Blocks(theme=Base(), title="Question Answering App using Vector Search +
 
 # Call query_data function upon clicking the Submit button
 
-    button.click(query_data, textbox, openai_api_key, outputs=[output1, output2])
+    button.click(query_data,
+                 inputs=[textbox, openai_api_key, mongo_uri],
+                 outputs=[output1, output2]
+                )
 
 demo.launch()