mv1

app.py

@@ -64,6 +64,19 @@ def generate_chart(data: Union[Dict, List[Dict], pd.DataFrame],
         A Plotly Figure object (interactive) or None on error
     """
     try:
+        # Debug: Log the input data
+        logger.info(f"Generating {chart_type} chart with x={x}, y={y}")
+        logger.info(f"Input data type: {type(data)}")
+        if hasattr(data, 'head'):  # It's a DataFrame
+            logger.info(f"DataFrame columns: {data.columns.tolist()}")
+            logger.info(f"Data sample:\n{data.head(2).to_string()}")
+        elif isinstance(data, (list, dict)):
+            logger.info(f"Data sample: {str(data)[:500]}...")
+            
+        # Validate data
+        if data is None or (hasattr(data, 'empty') and data.empty) or (isinstance(data, (list, dict)) and not data):
+            logger.error("No data provided to generate_chart")
+            return None
         # Convert data to DataFrame if it's a list of dicts
         if isinstance(data, list):
             df = pd.DataFrame(data)
@@ -77,17 +90,50 @@ def generate_chart(data: Union[Dict, List[Dict], pd.DataFrame],
             
         # Generate the appropriate chart type
         fig = None
-        if chart_type == 'bar':
-            fig = px.bar(df, x=x, y=y, title=title)
-        elif chart_type == 'line':
-            fig = px.line(df, x=x, y=y, title=title)
-        elif chart_type == 'pie':
-            fig = px.pie(df, names=x, values=y, title=title, hole=0)
-        elif chart_type == 'scatter':
-            fig = px.scatter(df, x=x, y=y, title=title)
-        elif chart_type == 'histogram':
-            fig = px.histogram(df, x=x, title=title)
-        else:
+        try:
+            if chart_type == 'bar':
+                fig = px.bar(df, x=x, y=y, title=title)
+            elif chart_type == 'line':
+                fig = px.line(df, x=x, y=y, title=title)
+            elif chart_type == 'pie':
+                fig = px.pie(df, names=x, values=y, title=title, hole=0)
+            elif chart_type == 'scatter':
+                # Ensure we have valid numeric data for scatter plot
+                if df[x].isnull().any() or df[y].isnull().any():
+                    logger.error(f"Null values found in data for scatter plot. x: {df[x].isnull().sum()}, y: {df[y].isnull().sum()}")
+                    # Try to convert to numeric, coercing errors
+                    df[x] = pd.to_numeric(df[x], errors='coerce')
+                    df[y] = pd.to_numeric(df[y], errors='coerce')
+                    # Drop rows with NaN values
+                    df = df.dropna(subset=[x, y])
+                    if df.empty:
+                        logger.error("No valid numeric data points after cleaning")
+                        return None
+                    logger.info("Data cleaned for scatter plot")
+                fig = px.scatter(df, x=x, y=y, title=title)
+            elif chart_type == 'histogram':
+                fig = px.histogram(df, x=x, title=title)
+            else:
+                logger.error(f"Unsupported chart type: {chart_type}")
+                return None
+                
+            if fig is None:
+                logger.error("Failed to generate figure")
+                return None
+                
+        except Exception as e:
+            logger.error(f"Error generating {chart_type} chart: {str(e)}", exc_info=True)
+            # Try to provide a more helpful error message
+            if 'NoneType' in str(e) and 'heuristic' in str(e):
+                logger.error("A* search heuristic failed - likely due to invalid or missing coordinate data")
+                # Try to identify which column has the issue
+                for col in [x, y]:
+                    if col and col in df.columns:
+                        null_count = df[col].isnull().sum()
+                        if null_count > 0:
+                            logger.error(f"Column '{col}' has {null_count} null values")
+                        if not pd.api.types.is_numeric_dtype(df[col]):
+                            logger.error(f"Column '{col}' is not numeric. Type: {df[col].dtype}")
             return None
         
         # Update layout