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app.py

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+import os
+import traceback
+from pathlib import Path
+
+import gradio as gr
+
+from langchain.chat_models import init_chat_model
+from langchain.agents import create_agent
+from langchain_community.utilities import SQLDatabase
+from langchain_community.agent_toolkits import SQLDatabaseToolkit
+
+
+DB_PATH = Path("database_") / "zain_customer_360_ai_demo.db"
+MODEL_NAME = "gpt-4.1-mini"
+
+
+SYSTEM_PROMPT = """
+You are a telecom business intelligence SQL agent for Zain Jordan.
+
+You are connected to a SQLite Customer 360 database.
+
+Rules:
+- Use SELECT queries only.
+- Never modify the database.
+- Never use INSERT, UPDATE, DELETE, DROP, ALTER, TRUNCATE, CREATE, REPLACE, ATTACH, DETACH, or VACUUM.
+- Inspect the database schema before writing SQL.
+- Limit results to 10 rows unless the user asks otherwise.
+- Use only relevant columns.
+- Do not guess facts that are not in the database.
+- Explain the result in simple business language.
+
+Useful context:
+- Churn analysis usually uses customers and customer_churn_scores.
+- Revenue/value analysis usually uses customer_value_segments, invoices, payments, or transactions.
+- Complaint analysis usually uses complaints and support_interactions.
+- Campaign analysis usually uses campaigns and customer_campaign_responses.
+- Network analysis usually uses network_towers and network_events.
+
+Final answer format:
+1. Direct Answer
+2. Key Numbers
+3. Business Interpretation
+4. Recommended Next Action
+""".strip()
+
+
+sql_agent = None
+
+
+def get_sql_agent():
+    global sql_agent
+
+    if sql_agent is not None:
+        return sql_agent
+
+    api_key = os.environ.get("OPENAI_API_KEY")
+
+    if not api_key:
+        raise ValueError(
+            "OPENAI_API_KEY is missing. Add it in Hugging Face Space Settings → Variables and secrets → Secrets."
+        )
+
+    if not DB_PATH.exists():
+        raise FileNotFoundError(
+            f"Database not found at: {DB_PATH}\n\n"
+            "Please create a folder named database_ and upload zain_customer_360_ai_demo.db inside it."
+        )
+
+    db_uri = f"sqlite:///{DB_PATH.resolve()}"
+    db = SQLDatabase.from_uri(db_uri)
+
+    llm = init_chat_model(
+        MODEL_NAME,
+        model_provider="openai"
+    )
+
+    toolkit = SQLDatabaseToolkit(
+        db=db,
+        llm=llm
+    )
+
+    tools = toolkit.get_tools()
+
+    sql_agent = create_agent(
+        model=llm,
+        tools=tools,
+        system_prompt=SYSTEM_PROMPT
+    )
+
+    return sql_agent
+
+
+def run_sql_agent(question):
+    try:
+        if not question or not question.strip():
+            return "Please enter a business question."
+
+        agent = get_sql_agent()
+
+        result = agent.invoke({
+            "messages": [
+                {
+                    "role": "user",
+                    "content": question
+                }
+            ]
+        })
+
+        final_message = result["messages"][-1]
+
+        if hasattr(final_message, "content"):
+            return final_message.content
+
+        return str(final_message)
+
+    except Exception as e:
+        return f"""
+### Error
+
+{str(e)}
+
+### Traceback
+
+```text
+{traceback.format_exc()}
+```
+"""
+
+
+with gr.Blocks(title="Zain Jordan SQL Agent") as demo:
+
+    gr.Markdown("# Zain Jordan Customer 360 SQL Agent")
+
+    gr.Markdown("""
+Ask business questions from the Zain Jordan Customer 360 SQLite database.
+
+This app uses:
+
+- SQLite database
+- LangChain `SQLDatabase`
+- LangChain `SQLDatabaseToolkit`
+- OpenAI `gpt-4.1-mini`
+- Gradio interface
+
+Expected database location:
+
+```text
+database_/zain_customer_360_ai_demo.db
+```
+
+Example questions:
+
+- Which cities have the most high-risk churn customers?
+- Which customer value segments have the highest average ARPU?
+- What are the top complaint categories?
+- Which campaigns had the highest conversion rate?
+""")
+
+    question = gr.Textbox(
+        label="Ask a business question",
+        lines=4,
+        value="Which cities have the most high-risk churn customers? Show the top 10 cities.",
+        placeholder="Ask a question about churn, revenue, complaints, campaigns, customers, or network events."
+    )
+
+    run_button = gr.Button("Run SQL Agent", variant="primary")
+
+    answer = gr.Markdown(label="Answer")
+
+    run_button.click(
+        fn=run_sql_agent,
+        inputs=question,
+        outputs=answer
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+pandas
+sqlalchemy
+langchain
+langchain-openai
+langchain-community
+openai