Update app.py

app.py

@@ -4,7 +4,7 @@ import pandas as pd
 import duckdb
 import openai
 
-# 1) Read your OpenAI key from the Space’s Secrets
+# 1) Load your OpenAI key from the Space’s Secrets
 openai.api_key = os.getenv("OPENAI_API_KEY")
 
 # 2) Load your synthetic data into DuckDB
@@ -13,17 +13,17 @@ conn = duckdb.connect(':memory:')
 conn.register('sap', df)
 
 # 3) Build a one-line schema description for prompts
-schema = ", ".join(df.columns)  # e.g. "Region,Product,FiscalYear,FiscalQuarter,Revenue,Profit,ProfitMargin"
+schema = ", ".join(df.columns)
 
-# 4) SQL-generation via OpenAI
+# 4) Function to generate SQL via OpenAI
 def generate_sql(question: str) -> str:
-    system = (
+    system_prompt = (
         f"You are an expert SQL generator for a DuckDB table named `sap` "
         f"with columns: {schema}. "
-        "Translate the user’s question into a valid SQL query and return _only_ the SQL."
+        "Translate the user's question into a valid SQL query and return ONLY the SQL."
     )
     messages = [
-        {"role": "system", "content": system},
+        {"role": "system", "content": system_prompt},
         {"role": "user",   "content": question},
     ]
     resp = openai.ChatCompletion.create(
@@ -33,42 +33,41 @@ def generate_sql(question: str) -> str:
         max_tokens=150,
     )
     sql = resp.choices[0].message.content.strip()
-    # strip triple-backticks if present
+    # strip ``` if user or model wrapped it
     if sql.startswith("```") and sql.endswith("```"):
         sql = "\n".join(sql.splitlines()[1:-1])
     return sql
 
-# 5) Core QA function: NL → SQL → execute → format
+# 5) Core Q&A function: NL → SQL → execute → format
 def answer_profitability(question: str) -> str:
-    # a) generate SQL
+    # a) turn the question into SQL
     sql = generate_sql(question)
     # b) try to run it
     try:
         result_df = conn.execute(sql).df()
     except Exception as e:
         return (
-            f"❌ **Error executing SQL**\n\n"
-            f"```\n{e}\n```\n\n"
-            f"**Generated SQL**\n```sql\n{sql}\n```"
+            f"❌ Error executing SQL:\n{e}\n\n"
+            f"Generated SQL was:\n```sql\n{sql}\n```"
         )
     # c) format the result
     if result_df.empty:
-        return f"No rows returned.\n\n**Generated SQL**\n```sql\n{sql}\n```"
-    # single-cell → just the value
+        return f"No rows returned.\n\n```sql\n{sql}\n```"
+    # single-cell → scalar
     if result_df.shape == (1,1):
         return str(result_df.iat[0,0])
-    # otherwise, markdown table
+    # multi-cell → pretty table
     return result_df.to_markdown(index=False)
 
-# 6) Gradio UI
+# 6) Gradio interface with explicit outputs
 iface = gr.Interface(
     fn=answer_profitability,
-    inputs=gr.Textbox(lines=2, placeholder="Ask a question about profitability…"),
-    outputs=gr.Markdown(),             # renders errors, code, and tables nicely
+    inputs=gr.Textbox(lines=2, placeholder="Ask a question about profitability…", label="Question"),
+    outputs=gr.Textbox(lines=8, placeholder="Answer will appear here", label="Answer"),
     title="SAP Profitability Q&A (OpenAI → SQL → DuckDB)",
     description=(
         "Uses OpenAI’s GPT-3.5-Turbo to translate your question into SQL, "
-        "executes it on the `sap` table in DuckDB, and returns the result."
+        "executes it against the `sap` table in DuckDB, and returns the result."
     ),
     allow_flagging="never",
 )