import streamlit as st 
from crewai import Agent, Task, Crew, LLM
from crewai_tools import SerperDevTool
from gtts import gTTS
import speech_recognition as sr
import os 

gemini_key=os.getenv("gemini_api")


serper_key=os.getenv("serper_api")

search_tool = SerperDevTool()

recognizer = sr.Recognizer()

def recognize_speech():
    with sr.Microphone() as source:
        st.info("Listening... Speak now!")
        recognizer.adjust_for_ambient_noise(source)
        try:
            audio = recognizer.listen(source, timeout=5,stream=False)
            text = recognizer.recognize_google(audio)
            return text
        except sr.UnknownValueError:
            return "Sorry, could not understand the audio."
        except sr.RequestError:
            return "Could not request results, check your internet."
def text_input():
    text= st.text_input("Enter the answer")
    return (text)
    

# Initialize LLM
llm = LLM(model="gemini/gemini-1.5-flash",
          verbose=True,
          temperature=0.5,
          api_key=gemini_key)


# Define Agents
question_agent = Agent(
    role="interviewer",
    goal="Frame {number} questions based on the {job_description}",
    verbose=False,
    memory=True,
    backstory="You need to frame interview questions based on the job description.",
    llm=llm,
    tools=[search_tool], 
    allow_delegation=True
)

generator_agent = Agent(
    role="answer generator",
    goal="frame answer to {question} based on the {job_description}.",
    verbose=False,
    memory=True,
    backstory="you are expert in answering the question based on {job_description}.",
    llm=llm,
    tools=[search_tool],
    allow_delegation=True
)

evaluation_agent = Agent(
    role="evulation",
    goal="frame answer to {question} based on the {job_description}.",
    verbose=False,
    memory=True,
    backstory="you are expert in answering the question based on {job_description}.",
    llm=llm,
    tools=[search_tool],
    allow_delegation=True
)


# Define Tasks
question_task = Task(
    description="Generate {number}  interview questions based on the  {job_description} .",
    expected_output="A list of {number} questions.",
    tools=[search_tool],
    agent=question_agent
)

generator_task = Task(
    description="frame the answer to the {question} based on the {job_description}.",
    expected_output="Correct if the answer is right; otherwise, return the correct answer.",
    tools=[search_tool],
    agent=generator_agent
)


evaluation_task = Task(
    description="frame the answer to the {question} based on the {job_description}.",
    expected_output="Correct if the answer is right; otherwise, return the correct answer.",
    tools=[search_tool],
    agent=generator_agent
)

# Define Crews
crew1 = Crew(agents=[question_agent], tasks=[question_task])
crew2 = Crew(agents=[generator_agent], tasks=[generator_task])
crew3 = Crew(agents=[evaluation_agent],tasks=[evaluation_task])

# Initialize Streamlit App
st.title("Interview Preparation Bot")

# *Reset function to clear all session variables*
def reset_session():
    st.session_state.job_description = ""
    st.session_state.number=1
    st.session_state.questions = []
    st.session_state.current_question_index = 0
    st.session_state.answers = []
    st.session_state.evaluations = []
    st.session_state.completed = False

# Initialize session state variables
if "questions" not in st.session_state:
    reset_session()

if "number" not in st.session_state:
    st.session_state.number = 1

# User Input
st.session_state.job_description = st.text_input("Enter the Topic you need Practice", value=st.session_state.job_description)
st.session_state.number = st.number_input("Enter the Number of Question you need",min_value=1,value=st.session_state.number)

# Button to Generate Questions
if st.button("Start") and st.session_state.job_description and st.session_state.number:
    result = crew1.kickoff(inputs={"job_description": st.session_state.job_description,'number':st.session_state.number})
    st.session_state.questions = [q.strip(' ```') for q in result.raw.split("\n") if q.strip()]
    
    st.session_state.current_question_index = 0
    st.session_state.answers = []
    st.session_state.evaluations = []
    st.session_state.completed = False
    
# # Display Questions One by One
if st.session_state.questions and st.session_state.current_question_index < len(st.session_state.questions):
    question = st.session_state.questions[st.session_state.current_question_index]
    st.write(f"*Question {st.session_state.current_question_index + 1}:* {question}")
    tts = gTTS(question, lang="en")
    tts.save("response.mp3")
    audio_file = open("response.mp3", "rb")
    audio_bytes = audio_file.read()
    st.audio(audio_bytes, format="audio/mp3")
    
    # User enters answer
    type=st.radio("select the type",["Answer with voice ","Answer by type"])    

    if type=="Answer with voice ":
        output=""
        
        if st.button("Start Recording"):
            output = recognize_speech()
            st.write("Transcription: ", output)
            

    elif type=="Answer by type":
        
        output=st.text_input(f"Enter your answer for Question ")
        
        
    # st.session_state.answers .append(st.session_state.current_question_index + 1)

    answer=answer = f"{st.session_state.current_question_index + 1}{output}"

        
    # answer = st.text_input(f"Enter your answer for Question {st.session_state.current_question_index + 1}")

    if st.button("Submit Answer") and answer:
        # Evaluate Answer
        result2 = crew2.kickoff(inputs={"job_description": st.session_state.job_description, "question": question, "answer": answer})
        evaluation_result = result2.raw

        # Store answer and evaluation
        st.session_state.answers.append(answer)
        st.session_state.evaluations.append(evaluation_result)

        # Move to the next question
        st.session_state.current_question_index += 1

        # If all questions are answered, mark as completed
        if st.session_state.current_question_index == len(st.session_state.questions)-1:
            st.session_state.completed = True
col1,col2=st.columns(2)
# Show Evaluations After All Answers
if st.session_state.completed:
    with col2:
        if st.button("Review The Answers"):
            st.write("### Final Evaluation:")
            for i in range(len(st.session_state.questions)-1):
                st.write(f"*Q{i+1}:* {st.session_state.questions[i]}")
                st.write(f"*Your Answer:* {st.session_state.answers[i]}")
                st.write(f"*Evaluation:* {st.session_state.evaluations[i]}")
                st.write("---")