from crewai import Agent, Task, Crew
  
import os
import json
 


# import langchain libraries
# !pip install langchain langchain-groq langchainhub  duckduckgo-search
from langchain.agents import AgentExecutor
from langchain.agents import create_react_agent
from langchain.agents import create_structured_chat_agent
from langchain import hub
from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate
from langchain.agents import Tool
from langchain_community.tools import DuckDuckGoSearchResults
from langchain.schema.output_parser import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_community.tools import DuckDuckGoSearchRun
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains import create_retrieval_chain
from langchain import hub
from langchain.chains import RetrievalQA
from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain_community.document_loaders.csv_loader import CSVLoader
from langchain.tools import DuckDuckGoSearchRun
from langchain_core.output_parsers import JsonOutputParser
# from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate

#import gradio libraries
# !pip install  gradio gradio_client
import gradio as gr


#import vectorstore libraries
# !pip install faiss-cpu
from langchain_community.vectorstores import FAISS
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2") 



# Define the LLM - We shall use ChatGroq of Groq Platform and LLama70B
# This llm definition is redundant as now models will be chosen by user
llm = ChatGroq(
            api_key="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",
            model="llama3-70b-8192",
            # model = 'gemma-7b-it',
            temperature = 0
            # model = 'mixtral-8x7B-32768'
        )




import os
from langchain.document_loaders import TextLoader

# folder_path = "https://huggingface.co/spaces/nmurugesh/My-Interview-Chatbot/blob/main/"  # Replace with the actual path to your folder

file_names = ['banking.txt', 'certifications.txt','deeplearning.txt','domain-banking.txt',
'education.txt','imple.txt','personal.txt','presales.txt','publications.txt',
'summary.txt', 'requirements-eng.txt','testing.txt','work experience.txt']

documents = []
for filename in file_names:
    if filename.endswith(".txt"): 
        loader = TextLoader(filename)
        doc = loader.load()[0]  # Load the single Document object for the file
        documents.append(doc) 
vectorstore1 = FAISS.from_documents(documents, embedding_function)




vectorstore1.save_local("vectorstore1")



retriever1 = vectorstore1.as_retriever()


import os
 
os.environ["OPENAI_API_KEY"] = "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"  # This is what Crew AI expects 






# Agent 1: Interview candidate
Interview_candidate = Agent(
    llm = llm,
    role="Interview candidate",
    goal="You are currently attending an interview. Your objective is to ace the interview and get the job based on your qualifications and expertise",
    # tools = [scrape_tool, search_tool],
    verbose=True,
    backstory=(''' You are currently attending an interview.For all the questions asked, you SHOULD answer only from the context provided - \
    that is data provided for this purpose. Your answer should be confidently articulated, using professional tone and style, and concise and clear.
''' 
    )
)




# Task for Researcher Agent: Extract Job Requirements
Interview_answer_task = Task(
    description=("You are being interviewed by a company. You should answer based on information context provided. \
    The question is {question}. Context will be a {context}" 
    ),
    expected_output=("You are currently attending an interview.For all the questions asked, you SHOULD answer only from the context provided - \
    that is data provided for this purpose. Your answer should be confidently articulated, using professional tone and style, and concise and clear."
    ),
    agent=Interview_candidate
    # async_execution=True
)




interview_crew = Crew(
    agents=[Interview_candidate],

    tasks=[Interview_answer_task],

    verbose=True
)




def answer(question):

  result =interview_crew.kickoff(inputs={"question":question,
                               "context": "\n\n".join(doc.page_content for doc in retriever1.get_relevant_documents(question))  })
  
  return result

from gradio import Image

with gr.Blocks() as demo: 

  # Add a Markdown block for the description
  gr.Markdown("""<h1 style='color: blue;'>Interview Chatbot for N Murugesan</h1>""")
  gr.Markdown("""Powered by CrewAI,Gradio, Groq, Llama3, FAISS, Langchain""")
  
  gr.Markdown(
      """
       
      <h2 style = 'color: blue;'>
      This chatbot will answer interview questions on behalf of Murugesan Narayanaswamy! </h2>
      """
  )
  gr.Image("photo-recent.jpg", width=250)
  gr.Markdown("""<h2 style='color: blue;'>Ask any HR Round Interview Questions - Factual Answers based on Resume!</h2>""")
  # Use a Column to structure the inputs and outputs
  with gr.Column():  
      # First text input and button
        text_input1 = gr.Textbox(
          label="Enter your question here!",
          placeholder='''Ask your question; e.g., Tell something about yourself; Your career path has been diverse; \
          Could you walk us through the key transitions and the motivations behind those changes?''',
        
      )
        button1 = gr.Button("Answer!")
        outputs1 = [
          gr.Textbox(label="My Answer - you can verify with resume later!",show_copy_button=True)
      ]

        button1.click(answer,inputs=[text_input1], outputs=outputs1)

        
# Launch the Gradio app
demo.launch()