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def extract_overview_and_html(self, response_text):
    overview = ""
    match_overview = re.search(r"OVERVIEW_START([\s\S]*?)OVERVIEW_END", response_text)
    if match_overview:
        overview = match_overview.group(1).strip()

    html_code = ""
    match_html = re.search(r"HTML_START([\s\S]*?)HTML_END", response_text)
    if match_html:
        html_code = match_html.group(1).strip()

    return overview, html_code


template = config['configurable']['template']
        graph_data = [i.content for i in state['graph_data']]
        template = [{"section_data": graph_data[i], **sec} for i, sec in enumerate(template)]

        sections = '\n'.join('> {}. {}'.format(n, i['sectionTitle']) for n, i in enumerate(template, start=1))

        template_data_str = json.dumps(template, indent=2)
        prompt = f"""
You are a data analyst expert.
Task  description:-
- Produce a broad textual overview (as plain text) about what the data represents and what each visualization (chart/table) will depict.
and must contain the Plotly <script> plus all charts/tables described by each widget.
- For any chart types (as hinted by the 'widget_description'), choose the most appropriate Plotly visualization (donut pie chart, bar chart, table, etc.)
- After each chart in the HTML, include a short <p> or <div> element that briefly explains what that chart shows (the “detailed explanation”).
-Embed Plotly directly  so the file is self-contained (with external CDNs as needed).
-Do not place the overview inside the HTML. The overview and the HTML must be returned separately.
-Output Format:
- Wrap the textual overview in lines that say:
```
OVERVIEW_START
[Your plain-text overview here]
OVERVIEW_END
```
- Followed by:
```
HTML_START

HTML_END
```
- Do not include any extra text outside these markers.
-Generate {len(sections)} sections and {state['template_widgets']} from following sections data {sections}
This template comprises multiple sections that each contain widgets. each section has some widgets inside it. each widget comprise of one analysis chart/insight.
template data contains 
 {template_data_str}
        """

       
        llm_response = self.llm.invoke([HumanMessage(content=prompt)])
        response_text = llm_response.content
        overview_section, html_section = self.extract_overview_and_html(response_text)
        if html_section.strip()[:7].lower() != '```html':
            html_section = "```html " + html_section + " ```"

        
        # calculate tokens
        state['token_usage']['prompt_tokens'] += self.token_calculator(prompt)
        state['token_usage']['completion_tokens'] += self.token_calculator(response_text)
        state['token_usage']['total_tokens'] += self.token_calculator(prompt) + self.token_calculator(
            response_text)

        # return {"messages": llm_response}

        return {"messages": AIMessage(content=html_section), "overview_data": overview_section}