i want something like all the questions we find in the file we can make appear on screen 
correct my below code

this is my 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
from agents.qa_agent import QAAgent

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:
                    topics = agent.analyze_document(text, custom_prompt)
                    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
    summary = agent.generate_consolidated_summary_from_topics(processed_files, custom_prompt)
    
    # Store summary based on mode
    summary_keys = {
        "overall": 'consolidated_summary_overall',
        "tariff": 'consolidated_summary_tariff',
        "sales": 'consolidated_summary_sales',
        "supplychain": 'consolidated_summary_supplychain',
    }
    sess[summary_keys[mode]] = 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').replace('qa', 'Q&A')}.")

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': {},
            'processed_files_qa': {},
            '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,
            'consolidated_summary_qa': None,
            'last_analysis_mode': None,
            'custom_prompt_enabled': {}, 
            'custom_prompt_text': {}    
        }

    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.agent_qa = QAAgent(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..."):
                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
            },
            'qa': {
                'analyze': QAAgent.prompt_analyze,
            }
        }
    
    # Create tabs for each agent's custom prompts
    tab_overall, tab_tariff, tab_sales, tab_supply, tab_qa = st.tabs([
        "📑 Overall", "🔍 Tariff", "📈 Sales", "🚚 Supply Chain", "❓ Q&A"
    ])
    
    # 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"
                
    with tab_qa:
        st.subheader("Q&A Prompts")
        use_custom = st.checkbox("✏️ Use Custom Prompts", key="custom_qa")
        
        if use_custom:
            
            sess['custom_prompts']['qa']['analyze'] = st.text_area(
                "Analysis Prompt:",
                value=sess['custom_prompts']['qa']['analyze'],
                height=150,
                key="qa_prompt_analyze"
            )
            if st.button("🔄 Generate with Custom Prompts", key="generate_qa"):
                generate_mode = "qa"

    # --- Analysis Buttons ---
    st.subheader("Quick Analysis")
    col0, col1, col2, col3, col4 = st.columns(5)
    analyze_overall = col0.button("📑 Overall Summary")
    analyze_tariff = col1.button("🔍 Analyze Tariffs")
    analyze_sales = col2.button("📈 Analyze Sales")
    analyze_supplychain = col3.button("🚚 Supply Chain")
    analyze_qa = col4.button("❓ Q&A")

    # Handle generation requests
    if generate_mode or analyze_overall or analyze_tariff or analyze_sales or analyze_supplychain or analyze_qa:
        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
            "qa" if analyze_qa 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']
        elif mode == "qa":
            agent = st.session_state.agent_qa
            processed_files = sess['processed_files_qa']

        # 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
            elif mode == "qa":
                agent.prompt_analyze = QAAgent.prompt_analyze

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

    # --- 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
        },
        'qa': {
            'files': sess['processed_files_qa'],
            'sidebar_header': "Q&A by Document",
            'main_header': "❓ Questions & Answers",
            'agent': st.session_state.agent_qa
        }
    }

    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}",
                    use_container_width=True,
                ):
                    sess['active_topic'] = topic_name
                    sess['active_file'] = None

    elif last_mode == "qa":
        st.sidebar.subheader("Documents with Q&A")
        for filename in current_mode['files']:
            if st.sidebar.button(
                filename,
                key=f"qa_doc_{filename}",
                use_container_width=True
            ):
                sess['active_file'] = filename
                sess['active_topic'] = None

    else:
        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']}",
                            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 last_mode == "qa" and sess['active_file']:
        st.subheader(f"Q&A for {sess['active_file']}")
        filedata = current_mode['files'].get(sess['active_file'])
        
        if filedata and "topics" in filedata:
            # Display Q&A pairs
            for qa in filedata["topics"]:
                with st.expander(f"**Q:** {qa['question']}"):
                    st.markdown(f"**A:** {qa['answer']}")
                    st.caption(f"Asked by: {qa['asked_by']} | Answered by: {qa['answered_by']}")
        else:
            st.warning("Document not found in Q&A analysis")
    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
                
                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
        preserved_keys = [
            "client", "agent_overall", "agent_tariff",
            "agent_sales", "agent_supplychain", "agent_qa", "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
        st.session_state.update(preserved)

        # Reset session data
        st.session_state.session = {
            'uploaded_files': {},
            'processed_files_overall': {},
            'processed_files_tariff': {},
            'processed_files_sales': {},
            'processed_files_supplychain': {},
            'processed_files_qa': {},
            '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,
            'consolidated_summary_qa': None,
            'last_analysis_mode': None,
            'custom_prompt_enabled': {},
            'custom_prompt_text': {}
        }
        
        # Trigger rerun
        st.rerun()

if __name__ == "__main__":
    main()

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"

#     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.

# - Extract ALL Q&A pairs in the transcript
# - For each pair, include:
#   - The EXACT question text
#   - The EXACT answer text
#   - The speaker who asked the question
#   - The speaker who answered the question
#   - The timestamp or sequence number of the Q&A
  
# - 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, even if they seem minor
# - 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

# Transcript:
# {text}
# """

#     prompt_summary = """
# You are preparing a comprehensive Q&A report from multiple transcripts for Macy's executive team. 

# Organize the Q&A pairs by document and speaker. For each document:

# 1. List all Q&A pairs in the order they appeared
# 2. For each pair, include:
#    - The exact question and answer
#    - Speaker who asked
#    - Speaker who answered
# 3. Add a summary section highlighting:
#    - Most active speakers (who asked most questions)
#    - Key topics covered
#    - Most substantial responses

# Format the entire report as follows:

# # Document: [Document Name]
# ## Speaker: [Questioner Name]
# ### Q1: [Exact question text]
# **A:** [Exact answer text]  
# _Answered by: [Speaker Name]_

# ### Q2: [Exact question text]
# **A:** [Exact answer text]  
# _Answered by: [Speaker Name]_

# ## Summary:
# - Total questions: [number]
# - Most active questioner: [name] with [number] questions
# - Key topics: [list of topics]
# - Most substantial response: [brief description]

# Use the following extracted data:
# {topics_block}

# Note: Maintain professional tone and preserve all exact wording from the transcripts.
# """

#     prompt_detail = prompt_analyze
    
#     def analyze_document(self, text, custom_prompt=None):
#         prompt_analyze = custom_prompt if custom_prompt else self.prompt_analyze
#         return super().analyze_document(self.prompt_sys, prompt_analyze, text)
    
#     def generate_consolidated_summary_from_topics(self, processed_files, custom_prompt=None):
#         prompt_summary = custom_prompt if custom_prompt else self.prompt_summary
#         return super().generate_consolidated_summary_from_topics(self.prompt_sys, prompt_summary, processed_files)


# agents/qa_agent.py

from agents.base_agent import BaseAgent

class QAAgent(BaseAgent):
    prompt_sys = "Expert in extracting all Questions and Answers from transcripts for Macy's CEO"

    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}
"""


    prompt_detail = prompt_analyze
    
    def analyze_document(self, text, custom_prompt=None):
        prompt_analyze = custom_prompt if custom_prompt else self.prompt_analyze
        return super().analyze_document(self.prompt_sys, prompt_analyze, text)


give me full code