diff --git a/examples/demo.py b/examples/demo.py
new file mode 100644
index 0000000..2babb0c
--- /dev/null
+++ b/examples/demo.py
@@ -0,0 +1,40 @@
+# Import necessary modules
+from llama_cpp import Llama
+from wurlitzer import pipes
+import gradio as gr
+import duckdb
+from utils import generate_sql
+
+# Set up client with model path and context size
+with pipes() as (out, err):
+    client = Llama(
+        model_path="/data/workspace/DuckDB-NSQL-7B-v0.1/DuckDB-NSQL-7B-v0.1-q8_0.gguf",
+        n_ctx=2048,
+    )
+
+
+# Connect to DuckDB database
+con = duckdb.connect("nyc.duckdb")
+
+# This function will be the interface for Gradio
+def generate_sql_from_natural_language(question):
+    # Sample question for SQL generation
+    #question = "alter taxi table and add struct column with name test and keys a:int, b:double"
+
+    # Generate SQL, check validity, and print
+    sql = generate_sql(question, con, client)
+    print(sql)
+    return sql
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_sql_from_natural_language,
+    inputs=[gr.Textbox(lines=2, placeholder="Enter your SQL request in natural language here..."),],
+    outputs=["text"],
+    title="Natural Language to SQL",
+    description="Enter a SQL request in natural language to generate the corresponding SQL query."
+)
+
+# Launch the application
+iface.launch(server_name="0.0.0.0", server_port=7860)
+
diff --git a/requirements.txt b/requirements.txt
index 86a53fe..a74e03b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-manifest-ml[all]==0.1.8
+manifest-ml[all]==0.1.9
 pandas>=2.0.0
 sqlalchemy<2.0.0
 transformers>=4.34.1
@@ -23,4 +23,8 @@ packaging==23.2
 ninja==1.11.1.1
 flash-attn==2.3.3
 langchain
-pydantic>2
+pydantic
+wurlitzer==3.0.3
+llama_cpp_python==0.2.33
+gradio==3.50.0
+gradio_client==0.6.1