Create app.py

app.py

@@ -0,0 +1,50 @@
+import os
+import streamlit as st
+from dotenv import load_dotenv  # Import load_dotenv to load environment variables
+from langchain import HuggingFaceHub
+
+# Load environment variables from the .env file
+load_dotenv()
+
+# Set your Hugging Face API token from the environment variable
+HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
+
+# Function to return the SQL query from natural language input
+def load_sql_query(question):
+    try:
+        # Initialize the Hugging Face model using LangChain's HuggingFaceHub class
+        llm = HuggingFaceHub(
+            repo_id="Salesforce/grappa_large_jnt",  # Hugging Face model repo for text-to-SQL
+            huggingfacehub_api_token=HUGGINGFACE_API_TOKEN,  # Pass your API token
+            model_kwargs={"temperature": 0.3}  # Optional: Adjust response randomness
+        )
+        
+        # Call the model with the user's question and get the SQL query
+        sql_query = llm.predict(question)
+        return sql_query
+    except Exception as e:
+        # Capture and return any exceptions or errors
+        return f"Error: {str(e)}"
+
+# Streamlit App UI starts here
+st.set_page_config(page_title="Text-to-SQL Demo", page_icon=":robot:")
+st.header("Text-to-SQL Demo")
+
+# Function to get user input
+def get_text():
+    input_text = st.text_input("Ask a question (related to a database):", key="input")
+    return input_text
+
+# Get user input
+user_input = get_text()
+
+# Create a button for generating the SQL query
+submit = st.button('Generate SQL')
+
+# If the generate button is clicked and user input is not empty
+if submit and user_input:
+    response = load_sql_query(user_input)
+    st.subheader("Generated SQL Query:")
+    st.write(response)
+elif submit:
+    st.warning("Please enter a question.")  # Warning for empty input