updated prompt

main.py

@@ -147,7 +147,7 @@ async def process_analysis_request(request: Request):
         `analyze_image_content`:
         a. ** If you choose to use the `analyze_image_content` tool, you MUST provide both the `image_path` and a `prompt`. The `prompt` argument **MUST** contain all the specific questions the user has asked about the image, extracted from their main request.**
         b. **For `run_sql_query`, the `db_connection_string` must be formatted correctly. For an uploaded file named 'my_data.db', the correct string is `'sqlite:///my_data.db'`.**
-        c. **For `parse_html`, the `selectors` argument MUST be a JSON object mapping descriptive names to CSS selector strings. Example: `{"titles": "h2.article-title", "prices": ".price-tag"}`.**
+        c. **For `parse_html`, the `selectors` argument MUST be a JSON object mapping descriptive names to CSS selector strings. Example: `{{"titles": "h2.article-title", "prices": ".price-tag"}}`.**
         4.  **DATA CLEANING (IMPORTANT):
         a. ** When you load a CSV file using pandas, the column names might have leading/trailing whitespace. Your first step after loading the data MUST be to clean the column names. A good method is: `df.columns = df.columns.str.strip()`.**
         b. **When identifying columns, you MUST perform a case-insensitive match. A robust method is to convert all column names to lowercase for comparison, like this: `df_cols_lower = [c.lower() for c in df.columns]` and then search for your lowercase keywords (e.g., 'sales') in that list.**