Create app.py

app.py

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+import streamlit as st
+import openai
+import pyodbc
+import pandas as pd
+
+# -----------------------------
+# 🔐 1. Configuration
+# -----------------------------
+openai.api_key = "YOUR_OPENAI_API_KEY"
+
+# SQL Server connection string
+server = 'dcdocstgdb01.dc68032.easn.morningstar.com'
+database = 'DocumentAcquisition'
+username = 'DocuUser'
+password = 'StagTest@1'
+
+conn_str = (
+    f"DRIVER={{ODBC Driver 17 for SQL Server}};"
+    f"SERVER={server};"
+    f"DATABASE={database};"
+    f"UID={username};"
+    f"PWD={password}"
+)
+
+# -----------------------------
+# 🤖 2. Natural Language → SQL
+# -----------------------------
+def generate_sql(nl_question):
+    prompt = f"""
+You are a helpful assistant that converts user questions into SQL Server queries.
+
+Question: "{nl_question}"
+
+SQL Query:
+"""
+    response = openai.ChatCompletion.create(
+        model="gpt-4",  # or gpt-3.5-turbo
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0
+    )
+    sql_query = response.choices[0].message["content"].strip()
+    return sql_query
+
+# -----------------------------
+# 🧠 3. Run SQL and return results
+# -----------------------------
+def run_query(sql_query):
+    try:
+        conn = pyodbc.connect(conn_str)
+        df = pd.read_sql(sql_query, conn)
+        conn.close()
+        return df
+    except Exception as e:
+        return str(e)
+
+# -----------------------------
+# 🎛️ 4. Streamlit UI
+# -----------------------------
+st.title("🧠 GenAI SQL Server Q&A")
+st.markdown("Ask a question in natural language. I’ll generate SQL and return the results from your database.")
+
+user_question = st.text_input("📌 Ask your question:")
+
+if st.button("Submit") and user_question:
+    with st.spinner("Thinking..."):
+        sql_query = generate_sql(user_question)
+        st.code(sql_query, language="sql")
+        
+        result = run_query(sql_query)
+        
+        if isinstance(result, pd.DataFrame):
+            st.success("✅ Results:")
+            st.dataframe(result)
+        else:
+            st.error(f"❌ Error:\n{result}")