diff --git a/examples/UI/SLIM_extract_tool_streamlit.py b/examples/UI/SLIM_extract_tool_streamlit.py
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+++ b/examples/UI/SLIM_extract_tool_streamlit.py
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+""" This example provides a basic framework to build a Chatbot UI interface in conjunction with LLMWare
+    using Streamlit Chat UI.
+
+    To run this example requires an install of Streamlit, e.g., `pip3 install streamlit`
+
+    To execute the script, run from the command line with:  `streamlit run using_with_streamlit_ui.py`
+
+    Also, please note that the first time you run with a new model, the model will be downloaded and cached locally,
+    so expect a delay on the 'first run' which will be much faster on every successive run.
+
+    All components of the chatbot will be running locally, so the speed will be determined greatly by the
+    CPU/GPU capacities of your machine.
+
+    We have set the max_output at 250 tokens - for faster, set lower ...
+
+    For more information on the Streamlit Chat UI,
+    see https://docs.streamlit.io/develop/tutorials/llms/build-conversational-apps
+
+
+"""
+
+import streamlit as st
+from llmware.models import ModelCatalog
+
+# Title of the Streamlit app
+st.title("SLIM Extract Tool LLMWARE")
+
+# Text input for the text to analyze
+text_to_analyze = st.text_area("Enter the text to analyze:", "\"Good Will Hunting,\" a 1997 film directed by Gus Van Sant, tells the story of a young janitor at MIT who has a hidden talent for mathematics and undergoes therapy to confront his troubled past.")
+
+# Text input for the queries
+queries_input = st.text_area("Enter your queries (comma separated):", "Director, Film Name")
+
+# Convert the input queries to a list
+queries_list = [query.strip() for query in queries_input.split(',')]
+
+# Button to run the analysis
+if st.button("Analyze"):
+    # Load the model
+    model = ModelCatalog().load_model("slim-extract-tool", sample=False, temperature=0.0, max_output=250)
+    
+    # Initialize the output dictionary
+    output_dict = {}
+
+    # Loop through the queries and call the model with the entire text for each query
+    for j, query in enumerate(queries_list):
+        st.write(f"Query {j+1}: {query}")
+        response = model.function_call(text_to_analyze, function="extract", params=[query])
+        output_dict.update(response["llm_response"])
+        #if not response["llm_response"]:
+           # st.write("No response")
+       # st.write("Extract response: ", response["llm_response"])
+
+    # Display the response on the screen
+    st.write("Output Dictionary:")
+    st.json(output_dict)
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