# Import required libraries
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import gradio as gr
import matplotlib.pyplot as plt
import pandas as pd
from speechbrain.pretrained import Tacotron2, HIFIGAN, EncoderDecoderASR

# Load Hugging Face psychometric model
psych_model_name = "KevSun/Personality_LM"  # Big Five personality traits
psych_model = pipeline("text-classification", model=psych_model_name)

# Load ASR model
asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-rnnlm-librispeech", savedir="tmp_asr")

# Load TTS model
tts_model = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmp_tts")
voc_model = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmp_voc")

# Psychometric Test Questions
text_questions = [
    "How do you handle criticism?",
    "Describe a time when you overcame a challenge.",
    "What motivates you to work hard?"
]

audio_questions = [
    "What does teamwork mean to you?",
    "How do you handle stressful situations?"
]

# Function to analyze text response
def analyze_text_responses(responses):
    analysis = [psych_model(response)[0] for response in responses]
    traits = {response["label"]: response["score"] for response in analysis}
    return traits

# Function to handle TTS
def generate_audio_question(question):
    mel_output, alignment, _ = tts_model.encode_text(question)
    waveforms = voc_model.decode_batch(mel_output)
    return waveforms[0].numpy()

# Function to process audio response
def process_audio_response(audio):
    text_response = asr_model.transcribe_file(audio)
    return text_response

# Gradio interface functions
def text_part(candidate_name, responses):
    traits = analyze_text_responses(responses)
    df = pd.DataFrame(traits.items(), columns=["Trait", "Score"])
    plt.figure(figsize=(8, 6))
    plt.bar(df["Trait"], df["Score"], color="skyblue")
    plt.title(f"Psychometric Analysis for {candidate_name}")
    plt.xlabel("Traits")
    plt.ylabel("Score")
    plt.xticks(rotation=45)
    plt.tight_layout()
    return df, plt

def audio_part(candidate_name, audio_responses):
    text_responses = [process_audio_response(audio) for audio in audio_responses]
    traits = analyze_text_responses(text_responses)
    df = pd.DataFrame(traits.items(), columns=["Trait", "Score"])
    plt.figure(figsize=(8, 6))
    plt.bar(df["Trait"], df["Score"], color="lightcoral")
    plt.title(f"Audio Psychometric Analysis for {candidate_name}")
    plt.xlabel("Traits")
    plt.ylabel("Score")
    plt.xticks(rotation=45)
    plt.tight_layout()
    return df, plt

# Gradio UI
def chat_interface(candidate_name, text_responses, audio_responses):
    text_df, text_plot = text_part(candidate_name, text_responses)
    audio_df, audio_plot = audio_part(candidate_name, audio_responses)
    return text_df, text_plot, audio_df, audio_plot

text_inputs = [gr.Textbox(label=f"Response to Q{i+1}: {q}") for i, q in enumerate(text_questions)]
audio_inputs = [gr.Audio(label=f"Response to Q{i+1}: {q}", type="file") for i, q in enumerate(audio_questions)]

interface = gr.Interface(
    fn=chat_interface,
    inputs=[gr.Textbox(label="Candidate Name")] + text_inputs + audio_inputs,
    outputs=["dataframe", "plot", "dataframe", "plot"],
    title="Psychometric Analysis Chatbot"
)

# Launch chatbot
interface.launch()