this is main.py
import streamlit as st
from io import BytesIO
from datetime import datetime

# Assuming these are correctly defined and accessible
from utils.pdf_utils import extract_text
from clients.azure_client import AzureClient
from clients.gemini_client import GeminiClient

from agents.tariff_agent import TariffTradeAgent
from agents.sales_agent import SalesAgent
from agents.supply_chain_agent import SupplyChainAgent
from agents.overall_agent import OverallAgent
from agents.enhanced_chat_agent import EnhancedChatAgent

def analyze_documents(mode, agent, processed_files, sess, custom_prompt=None):
    
    # Process individual files
    for filename, file_bytes in sess['uploaded_files'].items():
        if filename not in processed_files:
            try:
                file_obj = BytesIO(file_bytes)
                file_obj.name = filename
                text = extract_text(file_obj)
                if text:
                    if mode == "overall":
                        topics = agent.analyze_document(text, custom_prompt)
                    else:
                        topics = agent.analyze_document(text, custom_prompt) # Pass custom_prompt to other agents too
                    processed_files[filename] = {
                        "text": text,
                        "topics": topics,
                        "context": text,
                    }
                    st.toast(f"✅ Processed {filename}")
                else:
                    st.error(f"❌ Failed to extract text for {filename}")
            except Exception as e:
                st.error(f"Error processing {filename}: {str(e)}")

    # Generate consolidated summary
    if mode == "overall":
        summary = agent.generate_consolidated_summary_from_topics(processed_files, custom_prompt)
    else:
        summary = agent.generate_consolidated_summary_from_topics(processed_files, custom_prompt) # Pass custom_prompt
    
    if mode == "overall":
        sess['consolidated_summary_overall'] = summary
    elif mode == "tariff":
        sess['consolidated_summary_tariff'] = summary
    elif mode == "sales":
        sess['consolidated_summary_sales'] = summary
    elif mode == "supplychain":
        sess['consolidated_summary_supplychain'] = summary

    sess['last_analysis_mode'] = mode
    sess['active_file'] = None
    sess['active_topic'] = None
    st.success(f"Analysis and summary generated for {mode.replace('supplychain', 'supply chain')}.")

def main():
    st.set_page_config(page_title="Macy's Document Analysis Pro", layout="wide")
    st.title("📄 Macy's Document Analysis & Chat Tool")

    # Mode selection
    provider_select = st.radio("Select AI Provider", ["Azure GPT", "Google Gemini"])

    # Session Initialization
    if "session" not in st.session_state:
        st.session_state.session = {
            'uploaded_files': {},
            'processed_files_overall': {},
            'processed_files_tariff': {},
            'processed_files_sales': {},
            'processed_files_supplychain': {},
            'active_file': None,
            'active_topic': None,
            'topic_details_cache': {},
            'chat_history': [],
            'consolidated_summary_overall': None,
            'consolidated_summary_tariff': None,
            'consolidated_summary_sales': None,
            'consolidated_summary_supplychain': None,
            'last_analysis_mode': None,
            'custom_prompt_enabled': {}, # New: To track if custom prompt is enabled for each mode
            'custom_prompt_text': {}     # New: To store custom prompt text for each mode
        }

    if "client" not in st.session_state or st.session_state.get("provider") != provider_select:
        if provider_select == "Azure GPT":
            st.session_state.client = AzureClient()
            is_azure = True
        else:
            st.session_state.client = GeminiClient()
            is_azure = False

        # Initialize agents
        st.session_state.agent_overall = OverallAgent(st.session_state.client, is_azure)
        st.session_state.agent_tariff = TariffTradeAgent(st.session_state.client, is_azure)
        st.session_state.agent_sales = SalesAgent(st.session_state.client, is_azure)
        st.session_state.agent_supplychain = SupplyChainAgent(st.session_state.client, is_azure)
        st.session_state.chat_agent = EnhancedChatAgent(st.session_state.client, is_azure)
        st.session_state.provider = provider_select

    sess = st.session_state.session

    # --- File Upload ---
    st.subheader("1. Upload Documents")
    uploaded_files = st.file_uploader(
        "Upload PDF documents",
        type=["pdf"],
        accept_multiple_files=True,
        key="file_uploader",
    )
    if uploaded_files:
        new_files_added = False
        for file in uploaded_files:
            if file.name not in sess['uploaded_files']:
                sess['uploaded_files'][file.name] = file.getvalue()
                new_files_added = True

        if new_files_added:
            with st.spinner("🔍 Analyzing new documents with Overall Agent..."):
                # Always run initial overall analysis for new files
                analyze_documents(
                    mode="overall",
                    agent=st.session_state.agent_overall,
                    processed_files=sess['processed_files_overall'],
                    sess=sess
                )

    # --- Custom Prompts & Analysis ---
    st.subheader("2. Custom Prompts & Analysis")
    
    # Initialize custom prompts in session state
    if 'custom_prompts' not in sess:
        sess['custom_prompts'] = {
            'overall': {
                'summary': OverallAgent.prompt_summary,
                'analyze': OverallAgent.prompt_analyze,
                'detail': OverallAgent.prompt_detail
            },
            'tariff': {
                'summary': TariffTradeAgent.prompt_summary,
                'analyze': TariffTradeAgent.prompt_analyze,
                'detail': TariffTradeAgent.prompt_detail
            },
            'sales': {
                'summary': SalesAgent.prompt_summary,
                'analyze': SalesAgent.prompt_analyze,
                'detail': SalesAgent.prompt_detail
            },
            'supplychain': {
                'summary': SupplyChainAgent.prompt_summary,
                'analyze': SupplyChainAgent.prompt_analyze,
                'detail': SupplyChainAgent.prompt_detail
            }
        }
    
    # Create tabs for each agent's custom prompts
    tab_overall, tab_tariff, tab_sales, tab_supply = st.tabs([
        "📑 Overall", "🔍 Tariff", "📈 Sales", "🚚 Supply Chain"
    ])
    
    # Store which mode we're generating for
    generate_mode = None
    
    with tab_overall:
        st.subheader("Overall Prompts")
        use_custom = st.checkbox("✏️ Use Custom Prompts", key="custom_overall")
        
        if use_custom:
            sess['custom_prompts']['overall']['summary'] = st.text_area(
                "Summary Prompt:",
                value=sess['custom_prompts']['overall']['summary'],
                height=150,
                key="overall_prompt_summary"
            )
            sess['custom_prompts']['overall']['analyze'] = st.text_area(
                "Analysis Prompt:",
                value=sess['custom_prompts']['overall']['analyze'],
                height=150,
                key="overall_prompt_analyze"
            )
            sess['custom_prompts']['overall']['detail'] = st.text_area(
                "Detail Prompt:",
                value=sess['custom_prompts']['overall']['detail'],
                height=150,
                key="overall_prompt_detail"
            )
            
            if st.button("🔄 Generate with Custom Prompts", key="generate_overall"):
                generate_mode = "overall"
    
    with tab_tariff:
        st.subheader("Tariff Prompts")
        use_custom = st.checkbox("✏️ Use Custom Prompts", key="custom_tariff")
        
        if use_custom:
            sess['custom_prompts']['tariff']['summary'] = st.text_area(
                "Summary Prompt:",
                value=sess['custom_prompts']['tariff']['summary'],
                height=150,
                key="tariff_prompt_summary"
            )
            sess['custom_prompts']['tariff']['analyze'] = st.text_area(
                "Analysis Prompt:",
                value=sess['custom_prompts']['tariff']['analyze'],
                height=150,
                key="tariff_prompt_analyze"
            )
            sess['custom_prompts']['tariff']['detail'] = st.text_area(
                "Detail Prompt:",
                value=sess['custom_prompts']['tariff']['detail'],
                height=150,
                key="tariff_prompt_detail"
            )
            
            if st.button("🔄 Generate with Custom Prompts", key="generate_tariff"):
                generate_mode = "tariff"
    
    with tab_sales:
        st.subheader("Sales Prompts")
        use_custom = st.checkbox("✏️ Use Custom Prompts", key="custom_sales")
        
        if use_custom:
            sess['custom_prompts']['sales']['summary'] = st.text_area(
                "Summary Prompt:",
                value=sess['custom_prompts']['sales']['summary'],
                height=150,
                key="sales_prompt_summary"
            )
            sess['custom_prompts']['sales']['analyze'] = st.text_area(
                "Analysis Prompt:",
                value=sess['custom_prompts']['sales']['analyze'],
                height=150,
                key="sales_prompt_analyze"
            )
            sess['custom_prompts']['sales']['detail'] = st.text_area(
                "Detail Prompt:",
                value=sess['custom_prompts']['sales']['detail'],
                height=150,
                key="sales_prompt_detail"
            )
            
            if st.button("🔄 Generate with Custom Prompts", key="generate_sales"):
                generate_mode = "sales"
    
    with tab_supply:
        st.subheader("Supply Chain Prompts")
        use_custom = st.checkbox("✏️ Use Custom Prompts", key="custom_supply")
        
        if use_custom:
            sess['custom_prompts']['supplychain']['summary'] = st.text_area(
                "Summary Prompt:",
                value=sess['custom_prompts']['supplychain']['summary'],
                height=150,
                key="supply_prompt_summary"
            )
            sess['custom_prompts']['supplychain']['analyze'] = st.text_area(
                "Analysis Prompt:",
                value=sess['custom_prompts']['supplychain']['analyze'],
                height=150,
                key="supply_prompt_analyze"
            )
            sess['custom_prompts']['supplychain']['detail'] = st.text_area(
                "Detail Prompt:",
                value=sess['custom_prompts']['supplychain']['detail'],
                height=150,
                key="supply_prompt_detail"
            )
            
            if st.button("🔄 Generate with Custom Prompts", key="generate_supplychain"):
                generate_mode = "supplychain"

    # --- Analysis Buttons ---
    st.subheader("Quick Analysis")
    col0, col1, col2, col3 = st.columns(4)
    analyze_overall = col0.button("📑 Overall Summary")
    analyze_tariff = col1.button("🔍 Analyze Tariffs")
    analyze_sales = col2.button("📈 Analyze Sales")
    analyze_supplychain = col3.button("🚚 Supply Chain")

    # Handle generation requests
    if generate_mode or analyze_overall or analyze_tariff or analyze_sales or analyze_supplychain:
        if not sess['uploaded_files']:
            st.error("Please upload documents first.")
            st.stop()

        mode = generate_mode or (
            "overall" if analyze_overall else
            "tariff" if analyze_tariff else
            "sales" if analyze_sales else
            "supplychain" if analyze_supplychain else None
        )
        
        agent = None
        processed_files = None

        if mode == "overall":
            agent = st.session_state.agent_overall
            processed_files = sess['processed_files_overall']
        elif mode == "tariff":
            agent = st.session_state.agent_tariff
            processed_files = sess['processed_files_tariff']
        elif mode == "sales":
            agent = st.session_state.agent_sales
            processed_files = sess['processed_files_sales']
        elif mode == "supplychain":
            agent = st.session_state.agent_supplychain
            processed_files = sess['processed_files_supplychain']

        # Update agent with custom prompts if used
        if generate_mode:
            agent.prompt_summary = sess['custom_prompts'][mode]['summary']
            agent.prompt_analyze = sess['custom_prompts'][mode]['analyze']
            agent.prompt_detail = sess['custom_prompts'][mode]['detail']
            st.toast(f"Using custom prompts for {mode} analysis")
        else:
            # Reset to default prompts
            if mode == "overall":
                agent.prompt_summary = OverallAgent.prompt_summary
                agent.prompt_analyze = OverallAgent.prompt_analyze
                agent.prompt_detail = OverallAgent.prompt_detail
            elif mode == "tariff":
                agent.prompt_summary = TariffTradeAgent.prompt_summary
                agent.prompt_analyze = TariffTradeAgent.prompt_analyze
                agent.prompt_detail = TariffTradeAgent.prompt_detail
            elif mode == "sales":
                agent.prompt_summary = SalesAgent.prompt_summary
                agent.prompt_analyze = SalesAgent.prompt_analyze
                agent.prompt_detail = SalesAgent.prompt_detail
            elif mode == "supplychain":
                agent.prompt_summary = SupplyChainAgent.prompt_summary
                agent.prompt_analyze = SupplyChainAgent.prompt_analyze
                agent.prompt_detail = SupplyChainAgent.prompt_detail

        with st.spinner(f"🔍 Analyzing {mode.replace('supplychain', 'supply chain')} documents..."):
            analyze_documents(
                mode, 
                agent, 
                processed_files, 
                sess
            )

    # ... rest of the code (main display, topic analysis, chat interface) ...
            # After analysis, ensure the display mode is correctly set for the next render
            sess['last_analysis_mode'] = mode
            st.rerun() # Rerun to refresh UI after analysis and prompt updates

    # --- Main Display ---
    last_mode = sess['last_analysis_mode']
    if last_mode is None:
        st.info("Select an analysis type to begin.")
        return

    # Consolidated data setup
    mode_config = {
        'overall': {
            'files': sess['processed_files_overall'],
            'summary': sess['consolidated_summary_overall'],
            'sidebar_header': "Overall Topics",
            'main_header': "🌟 Executive Summary",
            'agent': st.session_state.agent_overall
        },
        'tariff': {
            'files': sess['processed_files_tariff'],
            'summary': sess['consolidated_summary_tariff'],
            'sidebar_header': "Tariff Topics",
            'main_header': "🌟 Tariff Analysis",
            'agent': st.session_state.agent_tariff
        },
        'sales': {
            'files': sess['processed_files_sales'],
            'summary': sess['consolidated_summary_sales'],
            'sidebar_header': "Sales Topics",
            'main_header': "🌟 Sales Analysis",
            'agent': st.session_state.agent_sales
        },
        'supplychain': {
            'files': sess['processed_files_supplychain'],
            'summary': sess['consolidated_summary_supplychain'],
            'sidebar_header': "Supply Chain Topics",
            'main_header': "🌟 Supply Chain Analysis",
            'agent': st.session_state.agent_supplychain
        }
    }

    current_mode = mode_config[last_mode]

    # --- Sidebar Topics ---
    st.sidebar.header(current_mode['sidebar_header'])

    if last_mode == "overall":
        topics_by_file = {}
        for filename, filedata in current_mode['files'].items():
            for topic in filedata["topics"]:
                topics_by_file.setdefault(topic['name'], []).append({
                    'file': filename,
                    'score': topic['score']
                })

        a = sorted(topics_by_file.items(),
                                                key=lambda x: max(x[1], key=lambda y: y['score'])['score'],
                                                reverse=True)
        before_view_more , after_view_more = a[:3] , a[3:]

        for topic_name, occurrences in before_view_more:
            total_score = sum(occ['score'] for occ in occurrences)
            file_count = len(occurrences)
            btn_label = f"{topic_name} (📈 {total_score:.1f} | 📑 {file_count})"

            if st.sidebar.button(
                btn_label,
                key=f"overall_topic_{topic_name}",
                use_container_width=True,
            ):
                sess['active_topic'] = topic_name
                sess['active_file'] = None

        if st.sidebar.button('View More',key = '1234' , use_container_width=True):
            for topic_name, occurrences in after_view_more:
                total_score = sum(occ['score'] for occ in occurrences)
                file_count = len(occurrences)
                btn_label = f"{topic_name} (📈 {total_score:.1f} | 📑 {file_count})"

                if st.sidebar.button(
                    btn_label,
                    key=f"overall_topic_more_{topic_name}", # Changed key to avoid collision
                    use_container_width=True,
                ):
                    sess['active_topic'] = topic_name
                    sess['active_file'] = None

    else: # For Tariff, Sales, Supply Chain modes
        for filename, filedata in current_mode['files'].items():
            expanded = (filename == sess['active_file'])
            with st.sidebar.expander(f"📄 {filename}", expanded=expanded):
                topics = sorted(filedata["topics"], key=lambda t: t["score"], reverse=True)
                for topic in topics[:3]:
                    is_selected = (
                        sess['active_file'] == filename and sess['active_topic'] == topic['name']
                    )
                    style = "primary" if is_selected else "secondary"

                    if st.button(
                        f"• {topic['name']} (Score: {topic['score']})",
                        key=f"{last_mode}_topic_{filename}_{topic['name']}",
                        type=style,
                        use_container_width=True,
                    ):
                        sess['active_file'] = filename
                        sess['active_topic'] = topic['name']

                if len(topics) > 3 and st.sidebar.button(f'View More for {filename}',key = f'{filename}_view_more' ,use_container_width=True):
                    for topic in topics[3:]:
                        is_selected = (
                            sess['active_file'] == filename and sess['active_topic'] == topic['name']
                        )
                        style = "primary" if is_selected else "secondary"

                        if st.button(
                            f"• {topic['name']} (Score: {topic['score']})",
                            key=f"{last_mode}_topic_more_{filename}_{topic['name']}", # Changed key
                            type=style,
                            use_container_width=True,
                        ):
                            sess['active_file'] = filename
                            sess['active_topic'] = topic['name']

    # --- Topic Analysis ---
    st.subheader("Detailed Analysis")
    if last_mode == "overall" and sess['active_topic']:
        mentions = []
        for filename, filedata in current_mode['files'].items():
            for topic in filedata["topics"]:
                if topic['name'] == sess['active_topic']:
                    mentions.append({
                        'file': filename,
                        'summary': topic.get('summary', ''),
                        'score': topic['score'],
                        'context': filedata['context']
                    })

        with st.expander("📊 Topic Prevalence"):
            cols = st.columns(3)
            cols[0].metric("Total Documents", len(mentions))
            avg_relevance = (sum(m['score'] for m in mentions)/len(mentions)) if mentions else 0
            cols[1].metric("Average Relevance", f"{avg_relevance:.1f}")
            cols[2].metric("First Mentioned", mentions[0]['file'] if mentions else "N/A")

        if mentions:
            combined_context = "\n\n".join(f"=== {m['file']} ===\n{m['context']}" for m in mentions)
            key_cache = (last_mode, sess['active_topic'])
            if key_cache in sess['topic_details_cache']:
                st.markdown(sess['topic_details_cache'][key_cache])
            else:
                placeholder = st.empty()
                collected_text = ""
                try:
                    for chunk in current_mode['agent'].generate_topic_detail(combined_context, sess['active_topic']):
                        collected_text = chunk
                        placeholder.markdown(collected_text + (" ▌" if "▌" in collected_text else ""))
                    sess['topic_details_cache'][key_cache] = collected_text
                except Exception as e:
                    st.error(f"Analysis error: {str(e)}")
    elif sess['active_file'] and sess['active_topic']:
        key_cache = (last_mode, sess['active_file'], sess['active_topic'])
        if key_cache in sess['topic_details_cache']:
            st.markdown(sess['topic_details_cache'][key_cache])
        else:
            placeholder = st.empty()
            collected_text = ""
            try:
                for chunk in current_mode['agent'].generate_topic_detail(
                    current_mode['files'][sess['active_file']]['context'],
                    sess['active_topic'],
                ):
                    collected_text = chunk
                    placeholder.markdown(collected_text + (" ▌" if "▌" in collected_text else ""))
                sess['topic_details_cache'][key_cache] = collected_text
            except Exception as e:
                st.error(f"Analysis error: {str(e)}")
    else:
        st.info("Select a topic from the sidebar")

    st.markdown("---")

    # --- Consolidated Summary ---
    st.header(current_mode['main_header'])
    if current_mode['summary']:
        st.markdown(current_mode['summary'])
    else:
        st.info("Summary not available yet")

    st.divider()

    # --- Chat Interface ---
    st.subheader("💬 Document Chat")
    
    # File selection
    available_files = list(current_mode['files'].keys())
    selected_files = st.multiselect(
        "Select documents for context:",
        available_files,
        key=f"chat_selector_{last_mode}"
    )

    # Chat history
    for msg in sess['chat_history']:
        with st.chat_message(msg["role"]):
            st.markdown(msg["content"])

    # Question handling
    question = st.chat_input("Ask about the documents...")
    if question:
        if not selected_files:
            st.error("Please select documents first")
            st.stop()

        sess['chat_history'].append({
            "role": "user",
            "content": question,
            "timestamp": datetime.utcnow().isoformat(),
        })

        try:
            context = "\n\n".join([
                f"=== {fname} ===\n{current_mode['files'][fname]['context']}"
                for fname in selected_files
            ])

            with st.chat_message("assistant"):
                response_placeholder = st.empty()
                full_response = ""
                q_type = None
                
                # Ensure custom_prompt_text_for_mode is available here if needed for chat_agent
                # For now, it's used only for analysis in analyze_documents, adjust if chat_agent needs it.
                for chunk, q_type in st.session_state.chat_agent.generate_answer(question, context):
                    full_response = chunk
                    response_placeholder.markdown(full_response + " ▌")
                
                response_placeholder.markdown(full_response)
                
                sess['chat_history'].append({
                    "role": "assistant",
                    "content": full_response,
                    "timestamp": datetime.utcnow().isoformat(),
                    "analysis_type": q_type
                })

        except Exception as e:
            st.error(f"Chat error: {str(e)}")
            sess['chat_history'].append({
                "role": "assistant",
                "content": f"Error: {str(e)}",
                "timestamp": datetime.utcnow().isoformat(),
            })

    # Reset button
    if st.button("🔄 Reset Session", type="secondary"):
        # Preserve long-lived objects like client and agents to avoid overhead on re-init
        preserved_keys = [
            "client", "agent_overall", "agent_tariff",
            "agent_sales", "agent_supplychain", "chat_agent", "provider"
        ]
        preserved = {k: st.session_state[k] for k in preserved_keys if k in st.session_state}
        
        # Clear entire session state
        st.session_state.clear()
        
        # Restore preserved keys back
        st.session_state.update(preserved)

        # Reset session-specific stored data completely
        st.session_state.session = {
            'uploaded_files': {},
            'processed_files_overall': {},
            'processed_files_tariff': {},
            'processed_files_sales': {},
            'processed_files_supplychain': {},
            'active_file': None,
            'active_topic': None,
            'topic_details_cache': {},
            'chat_history': [],
            'consolidated_summary_overall': None,
            'consolidated_summary_tariff': None,
            'consolidated_summary_sales': None,
            'consolidated_summary_supplychain': None,
            'last_analysis_mode': None,
            'custom_prompt_enabled': {},
            'custom_prompt_text': {}
        }
        
        # Trigger a rerun for fresh UI without old content
        st.rerun()

if __name__ == "__main__":
    main()

this is my base agent
from utils.pdf_utils import parse_topics

class BaseAgent:
    def __init__(self, client, is_azure):
        self.client = client
        self.is_azure = is_azure

    def analyze_document(self, prompt_system, prompt_user, text):
        if self.is_azure:
            messages = [
                {"role": "system", "content": prompt_system},
                {"role": "user", "content": prompt_user + "\n\n" + text},
            ]
            response = self.client.generate(messages=messages, temperature=0)
            raw_text = response.choices[0].message.content.strip()
        else:
            raw_text = self.client.generate(prompt_system, prompt_user + "\n\n" + text)
        return parse_topics(raw_text)

    def generate_consolidated_summary_from_topics(self, prompt_system, prompt_user_template, processed_files):
        topics_block = ""
        for filename, filedata in processed_files.items():
            topics_block += f"### Topics from document: {filename}\n"
            for topic in sorted(filedata["topics"], key=lambda x: x["score"], reverse=True):
                topics_block += f"- **{topic['name']}**: {topic['summary']} (Relevance: {topic['score']})\n"
            topics_block += "\n"

        prompt_user = prompt_user_template.format(topics_block=topics_block)

        if self.is_azure:
            messages = [
                {"role": "system", "content": prompt_system},
                {"role": "user", "content": prompt_user},
            ]
            response = self.client.generate(messages=messages, temperature=0)
            return response.choices[0].message.content.strip()
        else:
            return self.client.generate(prompt_system, prompt_user)

    def generate_topic_detail(self, prompt_system, prompt_user_template, document_text, topic):
        prompt_user = prompt_user_template.format(text=document_text, topic=topic)
        if self.is_azure:
            messages = [
                {"role": "system", "content": prompt_system},
                {"role": "user", "content": prompt_user},
            ]
            response = self.client.generate(messages=messages, temperature=0, stream=True)
            
            collected_chunks = []
            for chunk in response:
                if hasattr(chunk, "choices") and chunk.choices:
                    delta = chunk.choices[0].delta
                    content = getattr(delta, "content", "")
                    if content:
                        collected_chunks.append(content)
                        yield "".join(collected_chunks) + "▌"
            
            # Final yield without cursor
            if collected_chunks:
                yield "".join(collected_chunks)
        else:
            # For Gemini, we'll simulate streaming with 3 chunks
            full_response = self.client.generate(prompt_system, prompt_user)
            chunks = self.preserve_formatting_chunks(full_response)
            
            accumulated = ""
            for chunk in chunks:
                accumulated += chunk
                yield accumulated + "▌"
            
            yield accumulated  # Final yield without cursor
    
    def preserve_formatting_chunks(self, text):
        """Split response while preserving Markdown formatting"""
        # Split by paragraphs first
        paragraphs = text.split('\n\n')
        
        chunks = []
        for para in paragraphs:
            if para.startswith('##') or para.startswith('###') or para.startswith('* '):
                # Preserve headers and lists as whole chunks
                chunks.append(para + '\n\n')
            else:
                # Split long paragraphs into sentences
                sentences = para.split('. ')
                current_chunk = ""
                for sentence in sentences:
                    if not sentence:
                        continue
                    if len(current_chunk) + len(sentence) > 150:
                        if current_chunk:
                            chunks.append(current_chunk.strip() + '. ')
                        current_chunk = sentence
                    else:
                        current_chunk += sentence + '. '
                if current_chunk:
                    chunks.append(current_chunk.strip() + '\n\n')
        
        return chunks


            

this is my qa agent
from agents.base_agent import BaseAgent

class QAAgent(BaseAgent):
    prompt_sys = "Expert in extracting all Questions and Answers from transcripts for Macy's CEO"
    
    # Add these prompts that were missing in your original code
    prompt_summary = "Summary of extracted Q&A pairs"
    prompt_detail = "Detailed Q&A view"
    
    prompt_analyze = """
You are an expert in analyzing transcripts of earnings calls or business meetings. Your task is to carefully review the provided transcript and extract EVERY question and its corresponding answer EXACTLY as they appear, including the speaker information.

- Focus specifically on the QUESTION AND ANSWER SECTION of the document
- Extract ALL Q&A pairs in this section without exception
- For each pair, include:
  - The EXACT question text
  - The EXACT answer text
  - The speaker who asked the question
  - The speaker who answered the question
  
- Format each Q&A pair as:
  Q[#]: [exact question text]
    Asked by: [speaker name]
  A: [exact answer text]
    Answered by: [speaker name]

- Do not skip any questions - capture 100% of questions in the Q&A section
- Do not paraphrase or summarize - use the exact words from the transcript
- Include follow-up questions and responses
- Preserve any numerical data, percentages, or specific terms exactly as spoken
- Ensure complete coverage of all questions regardless of length or importance

Transcript:
{text}
"""
    
    def analyze_question(self, text, custom_prompt=None):
        prompt_analyze = custom_prompt if custom_prompt else self.prompt_analyze
        return super().analyze_question(self.prompt_sys, prompt_analyze, text)


what i require in my main.py you can provide a similar QA button as tarrif, supplychain whihc can genrtate the QA from the document make the function in base agent 
give me full code of each file

we doesnot want in our qa agent any analysis or consolidate summary provide only with analyze_question

and analyze_question should be available in Base agent 

give me full correct code