from langchain_core.runnables.schema import StreamEvent
from gradio import ChatMessage
from climateqa.engine.chains.prompts import audience_prompts
from front.utils import make_html_source,parse_output_llm_with_sources,serialize_docs,make_toolbox,generate_html_graphs
import numpy as np

def init_audience(audience :str) -> str:
    if audience == "Children":
        audience_prompt = audience_prompts["children"]
    elif audience == "General public":
        audience_prompt = audience_prompts["general"]
    elif audience == "Experts":
        audience_prompt = audience_prompts["experts"]
    else:
        audience_prompt = audience_prompts["experts"]
    return audience_prompt

def handle_retrieved_documents(event: StreamEvent, history : list[ChatMessage], used_documents : list[str]) -> tuple[str, list[ChatMessage], list[str]]:
    """ 
    Handles the retrieved documents and returns the HTML representation of the documents
    
    Args:
        event (StreamEvent): The event containing the retrieved documents
        history (list[ChatMessage]): The current message history
        used_documents (list[str]): The list of used documents
        
    Returns:
        tuple[str, list[ChatMessage], list[str]]: The updated HTML representation of the documents, the updated message history and the updated list of used documents
    """
    try:
        docs = event["data"]["output"]["documents"]
        docs_html = []
        textual_docs = [d for d in docs if d.metadata["chunk_type"] == "text"]
        for i, d in enumerate(textual_docs, 1):
            if d.metadata["chunk_type"] == "text":
                docs_html.append(make_html_source(d, i))
        
        used_documents = used_documents + [f"{d.metadata['short_name']} - {d.metadata['name']}" for d in docs]
        if used_documents!=[]:
            history[-1].content = "Adding sources :\n\n - " + "\n - ".join(np.unique(used_documents))
            
        docs_html = "".join(docs_html)
        
        related_contents = event["data"]["output"]["related_contents"]
         
    except Exception as e:
        print(f"Error getting documents: {e}")
        print(event)
    return docs, docs_html, history, used_documents, related_contents

def stream_answer(history: list[ChatMessage], event : StreamEvent, start_streaming : bool, answer_message_content : str)-> tuple[list[ChatMessage], bool, str]:
    """ 
    Handles the streaming of the answer and updates the history with the new message content
    
    Args:
        history (list[ChatMessage]): The current message history
        event (StreamEvent): The event containing the streamed answer
        start_streaming (bool): A flag indicating if the streaming has started
        new_message_content (str): The content of the new message
        
    Returns:
        tuple[list[ChatMessage], bool, str]: The updated history, the updated streaming flag and the updated message content
    """
    if start_streaming == False:
        start_streaming = True
        history.append(ChatMessage(role="assistant", content = ""))
    answer_message_content +=  event["data"]["chunk"].content
    answer_message_content = parse_output_llm_with_sources(answer_message_content)
    history[-1] = ChatMessage(role="assistant", content = answer_message_content)
    # history.append(ChatMessage(role="assistant", content = new_message_content))
    return history, start_streaming, answer_message_content

def handle_retrieved_owid_graphs(event :StreamEvent, graphs_html: str) -> str:
    """
    Handles the retrieved OWID graphs and returns the HTML representation of the graphs
    
    Args:
        event (StreamEvent): The event containing the retrieved graphs
        graphs_html (str): The current HTML representation of the graphs
        
    Returns:
        str: The updated HTML representation
    """
    try:
        recommended_content = event["data"]["output"]["recommended_content"]
        
        unique_graphs = []
        seen_embeddings = set()

        for x in recommended_content:
            embedding = x.metadata["returned_content"]
            
            # Check if the embedding has already been seen
            if embedding not in seen_embeddings:
                unique_graphs.append({
                    "embedding": embedding,
                    "metadata": {
                        "source": x.metadata["source"],
                        "category": x.metadata["category"]
                    }
                })
                # Add the embedding to the seen set
                seen_embeddings.add(embedding)


        categories = {}
        for graph in unique_graphs:
            category = graph['metadata']['category']
            if category not in categories:
                categories[category] = []
            categories[category].append(graph['embedding'])

        
        for category, embeddings in categories.items():
            graphs_html += f"<h3>{category}</h3>"
            for embedding in embeddings:
                graphs_html += f"<div>{embedding}</div>"
                
                
    except Exception as e:
        print(f"Error getting graphs: {e}")
        
    return graphs_html