from dotenv import load_dotenv
import os
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import OpenAIEmbeddings
import streamlit as st
from textwrap import dedent
from Prompts_and_Chains import LLMChains
from Templates import json_structure
import json
from Utils import estimate_to_value


class RFPProcessor:
    def __init__(self):
        load_dotenv()
        self.openai_api_key = os.getenv("OPENAI_API_KEY")
        self.chains_obj = LLMChains()

    def generate_estimations(self, tech_leads, senior_developers, junior_developers):
        print(st.session_state["user_stories_json"])
        inputs = {
            "project_summary": st.session_state["rfp_summary"],
            "user_stories": st.session_state["user_stories_json"],
            "tech_leads": tech_leads,
            "senior_developers": senior_developers,
            "junior_developers": junior_developers,
        }

        data = self.chains_obj.estimations_chain.run(inputs)
        estimation_json_data = json.loads(data)

        for epic_data in estimation_json_data["epics"]:
            epic = epic_data["name"]
            for feature_data in epic_data["features"]:
                feature = feature_data["name"]
                for story in feature_data["stories"]:
                    average = estimate_to_value(story["estimate"])
                    st.session_state.estimation_data.append(
                        {
                            "epic": epic,
                            "Feature": feature,
                            "Story Description": story["description"],
                            "Story Estimation": story["estimate"],
                            "Story Effort": story["effort"],
                            "Average Estimate": average,
                            "Story Rationale": story["rationale"],
                        }
                    )
        st.session_state["is_estimation_data_created"] = True

    def process_rfp_data(self, project_name, file):
        if project_name and file:
            loader = PdfReader(file)
            for i, page in enumerate(loader.pages):
                content = page.extract_text()
                if content:
                    temp = st.session_state["rfp_details"]
                    st.session_state["rfp_details"] = temp + content

            text_splitter = CharacterTextSplitter(
                separator="\n", chunk_size=1000, chunk_overlap=150, length_function=len
            )

            texts = text_splitter.split_text(st.session_state["rfp_details"])

            st.session_state["vectorstore"] = Chroma().from_texts(
                texts, embedding=OpenAIEmbeddings(openai_api_key=self.openai_api_key)
            )

            st.session_state.project_name = project_name
            st.session_state["rfp_summary"] = self.chains_obj.summary_chain.run(
                {
                    "project_name": st.session_state["project_name"],
                    "rfp_details": dedent(st.session_state["rfp_details"]),
                }
            )

            st.session_state["is_data_processed"] = True
            st.success("data processed sucessfully")

    def genrate_bot_result(self):
        if len(st.session_state["input"]) > 0:
            db = st.session_state["vectorstore"]
            context = db.similarity_search(st.session_state["input"])
            inputs = {
                "context": context[0].page_content,
                "input": st.session_state["input"],
            }
            output = self.chains_obj.bot_chain.run(inputs)
            st.session_state.past.append(st.session_state["input"])
            st.session_state.generated.append(output)
            st.session_state["input"] = ""

    def genrate_user_stories(self):
        output = self.chains_obj.user_story_chain.run(
            {
                "project_name": st.session_state["project_name"],
                "rfp_details": st.session_state["rfp_details"],
            }
        )
        st.session_state["user_stories"] = output

        json_response = self.chains_obj.json_chain.run(
            {
                "user_stories": st.session_state["user_stories"],
                "json_structure": json_structure,
            }
        )
        user_stories_data = json.loads(json_response)
        print(user_stories_data)
        st.session_state["user_stories_json"] = user_stories_data

        for epic_data in user_stories_data["epics"]:
            epic = epic_data["name"]
            for feature_data in epic_data["features"]:
                feature = feature_data["name"]
                for story in feature_data["stories"]:
                    st.session_state.user_stories_data.append(
                        {
                            "epic": epic,
                            "Feature": feature,
                            "Story Description": story["description"],
                        }
                    )
        st.session_state["is_user_stories_created"] = True