import streamlit as st
import random
import time
from transformers import pipeline
import speech_recognition as sr
from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
import numpy as np
import io

# Streamlit Configuration
st.set_page_config(page_title="Interview Copilot", layout="wide")
st.markdown(
    """
    <style>
    @import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;600;700&display=swap');
    html, body, [class*="css"]  {
        font-family: 'Poppins', sans-serif;
    }
    </style>
    """, unsafe_allow_html=True)

# Page Title
st.title("🎙️ Interview Copilot")
st.write("### Conduct professional mock interviews, receive feedback, and ace your next job opportunity!")

# Step 1: Input Job Title
st.subheader("Step 1: Enter Job Details")
job_title = st.text_input("Enter Job Title:", placeholder="e.g., Data Scientist, Software Engineer")
job_description = st.text_area("Enter Job Description:", placeholder="Provide details about the role")

# Step 2: Upload CV
st.subheader("Step 2: Upload Your CV")
uploaded_cv = st.file_uploader("Upload Your CV (PDF or Text):", type=["pdf", "txt"])
if uploaded_cv:
    cv_content = uploaded_cv.read().decode("utf-8") if uploaded_cv.type == "text/plain" else "Uploaded PDF file"

# Step 3: Start Interview
st.subheader("Step 3: Start the Interview")
if st.button("Start Interview"):
    if not job_title or not job_description or not uploaded_cv:
        st.error("Please fill in all the required fields (Job Title, Description, CV)!")
    else:
        # Predefined questions
        questions = [
            "Introduce yourself.",
            "Why do you want this job?",
        ]
        technical_questions = [
            "Tell me about a challenging project you've worked on.",
            f"What skills do you bring to the role of {job_title}?",
        ]
        non_technical_questions = [
            "How do you handle conflict in a team setting?",
            "What are your strengths and weaknesses?",
        ]

        # Add random questions to interview
        random_questions = random.sample(technical_questions, 2) + random.sample(non_technical_questions, 2)
        all_questions = questions + random_questions

        # Session state to track question index and responses
        if 'question_index' not in st.session_state:
            st.session_state.question_index = 0
        if 'responses' not in st.session_state:
            st.session_state.responses = []

        # Step 4: Question and Live Voice Response
        current_question = all_questions[st.session_state.question_index]
        st.write(f"**Question {st.session_state.question_index + 1}:** {current_question}")
        st.write("🎤 Please record your answer using the microphone.")
        
        # Initialize WebRTC for live audio capture
        audio_recorder = webrtc_streamer(key="audio-recorder", video_transformer_factory=VideoTransformerBase)

        if audio_recorder and audio_recorder.audio:
            audio_data = audio_recorder.audio
            audio_bytes = audio_data.tobytes()  # Convert audio to byte format for processing
            audio_file = io.BytesIO(audio_bytes)  # Use BytesIO to simulate file for audio processing

            # Process the audio
            recognizer = sr.Recognizer()
            try:
                with sr.AudioFile(audio_file) as source:
                    audio_data = recognizer.record(source)
                    # Transcribe audio to text
                    transcript = recognizer.recognize_google(audio_data)
                    st.session_state.responses.append(transcript)
                    st.write(f"📝 Transcribed Response: {transcript}")

                    # Proceed to next question or complete interview
                    if st.session_state.question_index < len(all_questions) - 1:
                        st.session_state.question_index += 1
                    else:
                        st.session_state.question_index = 0  # Reset to first question after the last one

                    # Show the next question or finish the interview
                    if len(st.session_state.responses) < len(all_questions):
                        st.experimental_rerun()

            except sr.UnknownValueError:
                st.error("Sorry, I couldn't understand the audio.")
            except sr.RequestError as e:
                st.error(f"Could not request results from Google Speech Recognition service; {e}")

        # Show the evaluation once all responses are collected
        if len(st.session_state.responses) == len(all_questions):
            st.write("### Interview Completed!")
            st.subheader("📊 Evaluation")
            sentiment_analyzer = pipeline("sentiment-analysis")
            positive_responses = sum(1 for response in st.session_state.responses if sentiment_analyzer(response)[0]["label"] == "POSITIVE")

            # Score Calculation
            score = (positive_responses / len(all_questions)) * 100
            if score >= 70:
                st.success(f"🎉 Excellent! You scored {score:.2f}/100. You're ready for the job!")
            else:
                st.error(f"🙁 You scored {score:.2f}/100. Here's how you can improve:")
                st.write("- Work on your communication clarity.")
                st.write("- Practice using correct grammar and vocabulary.")
                st.write("- Focus on pronunciation and speaking confidently.")
        
            # CV Feedback
            st.subheader("📄 CV Recommendations")
            st.write("- Add quantifiable achievements to your CV.")
            st.write("- Tailor your CV to the job description.")