import streamlit as st
import json
import textwrap
from typing import Dict, Any, List
from sql_formatter.core import format_sql
from langchain.callbacks.streamlit.streamlit_callback_handler import LLMThought, StreamlitCallbackHandler
from langchain.schema.output import LLMResult
from streamlit.delta_generator import DeltaGenerator

class ChatDataSelfSearchCallBackHandler(StreamlitCallbackHandler):
    def __init__(self) -> None:
        self.progress_bar = st.progress(value=0.0, text="Working...")
        self.tokens_stream = ""
    
    def on_llm_start(self, serialized, prompts, **kwargs) -> None:
        pass
        
    def on_text(self, text: str, **kwargs) -> None:
        self.progress_bar.progress(value=0.2, text="Asking LLM...")
        
    def on_chain_end(self, outputs, **kwargs) -> None:
        self.progress_bar.progress(value=0.6, text='Searching in DB...')
        if 'repr' in outputs:
            st.markdown('### Generated Filter')
            st.markdown(f"```python\n{outputs['repr']}\n```", unsafe_allow_html=True)
    
    def on_chain_start(self, serialized, inputs, **kwargs) -> None:
        pass

class ChatDataSelfAskCallBackHandler(StreamlitCallbackHandler):
    def __init__(self) -> None:
        self.progress_bar = st.progress(value=0.0, text='Searching DB...')
        self.status_bar = st.empty()
        self.prog_value = 0.0
        self.prog_map = {
            'langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain': 0.2,
            'langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain': 0.4,
            'langchain.chains.combine_documents.stuff.StuffDocumentsChain': 0.8
        }

    def on_llm_start(self, serialized, prompts, **kwargs) -> None:
        pass
        
    def on_text(self, text: str, **kwargs) -> None:
        pass
        
    def on_chain_start(self, serialized, inputs, **kwargs) -> None:
        cid = '.'.join(serialized['id']) 
        if cid != 'langchain.chains.llm.LLMChain':
            self.progress_bar.progress(value=self.prog_map[cid], text=f'Running Chain `{cid}`...')
            self.prog_value = self.prog_map[cid]
        else:
            self.prog_value += 0.1
            self.progress_bar.progress(value=self.prog_value, text=f'Running Chain `{cid}`...')

    def on_chain_end(self, outputs, **kwargs) -> None:
        pass
    

class ChatDataSQLSearchCallBackHandler(StreamlitCallbackHandler):
    def __init__(self) -> None:
        self.progress_bar = st.progress(value=0.0, text='Writing SQL...')
        self.status_bar = st.empty()
        self.prog_value = 0
        self.prog_interval = 0.2

    def on_llm_start(self, serialized, prompts, **kwargs) -> None:
        pass
        
    def on_llm_end(
        self,
        response: LLMResult,
        *args,
        **kwargs,
    ):
        text = response.generations[0][0].text
        if text.replace(' ', '').upper().startswith('SELECT'):
            st.write('We generated Vector SQL for you:')
            st.markdown(f'''```sql\n{format_sql(text, max_len=80)}\n```''')
            print(f"Vector SQL: {text}")
            self.prog_value += self.prog_interval
            self.progress_bar.progress(value=self.prog_value, text="Searching in DB...")
        
    def on_chain_start(self, serialized, inputs, **kwargs) -> None:
        cid = '.'.join(serialized['id']) 
        self.prog_value += self.prog_interval
        self.progress_bar.progress(value=self.prog_value, text=f'Running Chain `{cid}`...')
   
    def on_chain_end(self, outputs, **kwargs) -> None:
        pass
    
class ChatDataSQLAskCallBackHandler(ChatDataSQLSearchCallBackHandler):
    def __init__(self) -> None:
        self.progress_bar = st.progress(value=0.0, text='Writing SQL...')
        self.status_bar = st.empty()
        self.prog_value = 0
        self.prog_interval = 0.1
        
        
class LLMThoughtWithKB(LLMThought):
    def on_tool_end(self, output: str, color: str | None = None, observation_prefix: str | None = None, llm_prefix: str | None = None, **kwargs: Any) -> None:
        try:
            self._container.markdown("\n\n".join(["### Retrieved Documents:"] + \
                [f"**{i+1}**: {textwrap.shorten(r['page_content'], width=80)}" 
                 for i, r in enumerate(json.loads(output))]))
        except Exception as e:
            super().on_tool_end(output, color, observation_prefix, llm_prefix, **kwargs)
    
        
class ChatDataAgentCallBackHandler(StreamlitCallbackHandler):
    
    def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> None:
        if self._current_thought is None:
            self._current_thought = LLMThoughtWithKB(
                parent_container=self._parent_container,
                expanded=self._expand_new_thoughts,
                collapse_on_complete=self._collapse_completed_thoughts,
                labeler=self._thought_labeler,
            )

        self._current_thought.on_llm_start(serialized, prompts)