Create app.py

app.py

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+import streamlit as st
+import openai
+import pandas as pd
+from sqlalchemy import create_engine
+from langchain.chat_models import ChatOpenAI
+from langchain.utilities.sql_database import SQLDatabase
+from langchain.tools.sql.toolkit import SQLDatabaseToolkit
+
+# Set OpenAI API Key
+openai.api_key = "sk-O7esHSo2XAWm-GXUGXp7_P9l4qXrQMn0CIGzs34ojLT3BlbkFJeXGSSvywppRTAvyT0zZkmZLZsj5cg7XkAkBTh8ZxoA"
+
+# Database connection
+DATABASE_URL = "sqlite:///Sakila.db"  # Replace with your DB path or connection string
+engine = create_engine(DATABASE_URL)
+
+# Set up LangChain components
+llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.5)  # OpenAI's Chat model for LLM
+db = SQLDatabase(engine)  # Connect LangChain to the database
+toolkit = SQLDatabaseToolkit(llm=llm, db=db)  # Create the SQL toolkit
+
+# Streamlit UI setup
+st.title("SQL Data Chatbot with LangChain")
+st.write("Ask questions about the data, and I will answer them with both a response and an SQL query.")
+
+# Input field for the user question
+user_question = st.text_input("Your question:")
+
+# Process the question if provided
+if user_question:
+    # Generate the SQL query and answer using the toolkit
+    try:
+        # Execute the question through the SQL toolkit
+        answer = toolkit.query(user_question)
+
+        # Display the generated SQL query and answer
+        st.subheader("Generated SQL Query")
+        st.code(answer.query, language="sql")
+
+        # Display the generated answer
+        st.subheader("Answer")
+        st.write(answer.result)
+
+        # Execute the SQL query to get results
+        with engine.connect() as conn:
+            result_df = pd.read_sql_query(answer.query, conn)
+
+        # Show query results if any
+        if not result_df.empty:
+            st.write("Query Results:")
+            st.write(result_df)
+        else:
+            st.write("No results found for this query.")
+    
+    except Exception as e:
+        st.write(f"Error processing the query: {e}")