core/support_agent.py

import traceback
from typing import Dict, Any, List
from llama_cpp import Llama

from langchain_core.language_models.llms import LLM
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.prompts import PromptTemplate

class LlamaLangChain(LLM):
    llama_instance: Llama
    @property
    def _llm_type(self) -> str: return "custom"
    def _call(self, prompt: str, stop: List[str] | None = None, **kwargs) -> str:
        response = self.llama_instance(prompt, max_tokens=512, stop=stop, stream=False, echo=False)
        return response["choices"][0]["text"]
    async def _acall(self, prompt: str, stop: List[str] | None = None, **kwargs) -> str:
        return self._call(prompt, stop, **kwargs)

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

class SupportAgent:
    def __init__(self, llm_instance: Llama, embedding_path: str, db_path: str):
        print("--- Initializing Support Agent (Final Version) ---")
        if llm_instance is None: raise ValueError("SupportAgent received an invalid LLM instance.")
        self.langchain_llm_wrapper = LlamaLangChain(llama_instance=llm_instance)
        self.embeddings = HuggingFaceEmbeddings(model_name=embedding_path)
        self.vector_store = Chroma(persist_directory=db_path, embedding_function=self.embeddings)
        self.conversations: Dict[str, ConversationBufferMemory] = {}
        print("✅ Agent and core components initialized successfully.")

    def _get_or_create_memory(self, conversation_id: str) -> ConversationBufferMemory:
        if conversation_id not in self.conversations:
            self.conversations[conversation_id] = ConversationBufferMemory(memory_key="chat_history", return_messages=True, input_key="question", output_key='answer')
        return self.conversations[conversation_id]

    def answer(self, payload: dict, conversation_id: str) -> dict:
        question = payload.get("question", "")
        live_data_context = payload.get("live_data", "")
        user_role = payload.get("role", "user")
        
        memory = self._get_or_create_memory(conversation_id)

        try:
            # FINAL, POLISHED PROMPT
            human_friendly_template = """You are Sparky, a helpful AI assistant for Reachify.
Your job is to provide a direct and concise answer to the user's question.
Use the Live Data and Context to find the answer. Do not talk about yourself.

**Live Data (Facts from the user's account):**
{live_data}

**Context (General Knowledge):**
{context}

**Chat History:**
{chat_history}

**User's Question:** {question}

**Direct Answer:**
"""
            
            final_prompt = PromptTemplate.from_template(human_friendly_template)
            
            # ✅ SYNTAX FIX: Removed the space between 'as_' and 'retriever()'
            retriever = self.vector_store.as_retriever()

            qa_chain = ConversationalRetrievalChain.from_llm(
                llm=self.langchain_llm_wrapper, 
                retriever=retriever, 
                memory=memory, 
                combine_docs_chain_kwargs={"prompt": final_prompt}
            )
            
            result = qa_chain.invoke({ 
                "question": question,
                "live_data": live_data_context
            })

            raw_answer = result.get("answer", "I'm sorry, I could not find an answer.").strip()
            
            final_answer = raw_answer.split("Answer:")[0].split("Direct Answer:")[0].strip()

            return {"response": final_answer, "context": format_docs(result.get('source_documents', []))}

        except Exception as e:
            traceback.print_exc()
            return {"response": "A critical server error occurred in the AI agent.", "context": str(e)}


    def generate_caption_variant(self, caption: str, action: str) -> str:
        # Note: You were calling self.llm here but it's defined as self.langchain_llm_wrapper
        # This fixes that potential error.
        if not self.langchain_llm_wrapper:
            return "Error: The AI model is not available."

        system_prompt = (
            "You are an expert social media copywriter..." # your prompt here...
        )
        if action == 'improve_writing':
            user_instruction = "Improve the writing..."
        elif action == 'make_punchier':
            user_instruction = "Make it punchier..."
        elif action == 'generate_alternatives':
            user_instruction = "Generate three new, creative..."
        else:
            return "Error: Invalid action specified."

        final_prompt = f"[SYSTEM INSTRUCTIONS]..." # your full prompt composition...
        
        try:
            response = self.langchain_llm_wrapper._call(final_prompt) # Using _call directly
            return response.strip()
        except Exception as e:
            traceback.print_exc()
            return f"An error occurred while generating the caption."

    def generate_marketing_strategy(self, prompt: str) -> str:
        if not self.langchain_llm_wrapper: return "Error: The AI model is not available."
        try:
            return self.langchain_llm_wrapper._call(prompt)
        except Exception as e:
            traceback.print_exc()
            return f"An error occurred: {e}"

    def generate_content_outline(self, title: str) -> str:
        if not self.langchain_llm_wrapper: return "Error: The AI model is not available."
        prompt = f"""You are a professional content writer...
        **Title:** "{title}"
        **Your Outline:**
        """
        try:
            return self.langchain_llm_wrapper._call(prompt)
        except Exception as e:
            traceback.print_exc()
            return f"An error occurred: {e}"