Update app.py

app.py

@@ -1,5 +1,5 @@
 # --------------------------------------------------------------
-# gradio_s3_sql.py
+# gradio_s3_sql.py   (Gradio-compatible, no 'height' error)
 # --------------------------------------------------------------
 import boto3
 import pandas as pd
@@ -14,7 +14,6 @@ ACCESS_KEY = "rNuPBAQetemqpEeBospZ"
 SECRET_KEY = "BU4FccUYxzXVqiWjPSJM1CWEX1cNhBqbU9NeGidE"
 BUCKET_NAME = "accusagas3"
 
-# Initialize S3 client
 s3 = boto3.client(
     "s3",
     endpoint_url=ENDPOINT_URL,
@@ -24,31 +23,29 @@ s3 = boto3.client(
 
 
 def run_sql(path: str, sql: str) -> pd.DataFrame:
-    """Core function: S3 → CSV → DuckDB → SQL → DataFrame"""
-    # --- 1. Load CSV from S3 ---
+    """S3 → CSV → DuckDB → SQL → DataFrame (auto-cast + auto-fix)"""
+    # --- Load CSV ---
     try:
         obj = s3.get_object(Bucket=BUCKET_NAME, Key=path)
-        csv_content = obj["Body"].read().decode("utf-8")
-        df = pd.read_csv(StringIO(csv_content))
+        df = pd.read_csv(StringIO(obj["Body"].read().decode("utf-8")))
     except ClientError as e:
         if e.response["Error"]["Code"] == "NoSuchKey":
             return pd.DataFrame({"error": [f"File not found: {path}"]})
-        return pd.DataFrame({"error": [f"S3 Error: {str(e)}"]})
+        return pd.DataFrame({"error": [f"S3 Error: {e}"]})
     except Exception as e:
-        return pd.DataFrame({"error": [f"CSV read failed: {str(e)}"]})
+        return pd.DataFrame({"error": [f"CSV read failed: {e}"]})
 
     if df.empty:
         return pd.DataFrame({"error": ["CSV is empty"]})
 
-    # --- 2. Auto-convert numeric columns ---
+    # --- Auto-convert numeric columns ---
     numeric_keywords = ["price", "amount", "value", "cost", "revenue", "total", "volume", "open", "high", "low", "close"]
     for col in df.columns:
         if any(kw in col.lower() for kw in numeric_keywords):
-            # Clean: remove $, %, commas
             cleaned = df[col].astype(str).str.replace(r"[^\d.-]", "", regex=True)
             df[col] = pd.to_numeric(cleaned, errors="coerce")
 
-    # --- 3. Run SQL in DuckDB ---
+    # --- DuckDB ---
     con = duckdb.connect(":memory:")
     con.register("data", df)
 
@@ -60,7 +57,7 @@ def run_sql(path: str, sql: str) -> pd.DataFrame:
     try:
         result = con.execute(sql).df()
     except Exception as e:
-        # Auto-fix: CAST column to DOUBLE if type mismatch
+        # Auto-fix VARCHAR vs number
         if "Cannot compare values of type VARCHAR" in str(e):
             import re
             match = re.search(r"column ([a-zA-Z0-9_]+)", str(e), re.I)
@@ -71,7 +68,7 @@ def run_sql(path: str, sql: str) -> pd.DataFrame:
                     result = con.execute(fixed_sql).df()
                 except Exception as e2:
                     con.close()
-                    return pd.DataFrame({"error": [f"SQL failed even after cast: {e2}"]})
+                    return pd.DataFrame({"error": [f"SQL failed even after CAST: {e2}"]})
             else:
                 con.close()
                 return pd.DataFrame({"error": [f"Type error: {e}"]})
@@ -81,19 +78,18 @@ def run_sql(path: str, sql: str) -> pd.DataFrame:
     finally:
         con.close()
 
-    # Limit to 10,000 rows
     return result.head(10_000)
 
 
 # --------------------------------------------------------------
-# Gradio Interface
+# Gradio UI (no 'height' on Dataframe)
 # --------------------------------------------------------------
 with gr.Blocks(title="S3 SQL Query (iDrive e2)") as demo:
     gr.Markdown(
         """
         # S3 CSV SQL Explorer  
-        **Query any CSV in your iDrive e2 bucket using SQL**  
-        Table name: `data` | Auto-casts `Price`, `Amount`, etc. to numbers  
+        Query any CSV in your iDrive e2 bucket using SQL  
+        Table name: `data` | Auto-casts `Price`, `Amount`, etc.
         """
     )
 
@@ -110,14 +106,30 @@ with gr.Blocks(title="S3 SQL Query (iDrive e2)") as demo:
         )
 
     run_btn = gr.Button("Run SQL", variant="primary")
-    output = gr.Dataframe(
+
+    # Use DataGrid + CSS for fixed height
+    output = gr.DataGrid(
         label="Result",
         interactive=False,
         wrap=True,
-        height=500,
     )
 
-    # Click handler
+    # Apply height via CSS
+    demo.load(
+        None,
+        None,
+        None,
+        _js=f"""
+        () => {{
+            setTimeout(() => {{
+                const grid = document.querySelector('.gradio-container .data-grid');
+                if (grid) grid.style.maxHeight = '500px';
+                if (grid) grid.style.overflowY = 'auto';
+            }}, 100);
+        }}
+        """
+    )
+
     run_btn.click(
         fn=run_sql,
         inputs=[path_input, sql_input],
@@ -125,7 +137,6 @@ with gr.Blocks(title="S3 SQL Query (iDrive e2)") as demo:
         show_progress=True,
     )
 
-    # Examples
     gr.Examples(
         examples=[
             [
@@ -136,23 +147,10 @@ with gr.Blocks(title="S3 SQL Query (iDrive e2)") as demo:
                 "vatsav_123/reports/Gold Futures Historical Data.csv",
                 "SELECT MIN(Price) AS min_price, MAX(Price) AS max_price FROM data"
             ],
-            [
-                "vatsav_123/reports/Gold Futures Historical Data.csv",
-                "SELECT * FROM data WHERE Volume > 1000000 LIMIT 5"
-            ],
         ],
         inputs=[path_input, sql_input],
     )
 
-    gr.Markdown(
-        """
-        **Tips**  
-        - Use `data` as table name  
-        - Columns like `Price`, `Volume`, `Amount` are auto-converted to numbers  
-        - Invalid SQL → clear error message  
-        """
-    )
-
 
 # --------------------------------------------------------------
 # Launch
@@ -161,7 +159,6 @@ if __name__ == "__main__":
     demo.launch(
         server_name="0.0.0.0",
         server_port=7860,
-        share=False,  # Set True for public link
+        share=False,
         debug=True,
-        mcp_server=True
     )