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| import gradio as gr | |
| from transformers import pipeline | |
| from speechbrain.pretrained import Tacotron2, HIFIGAN, EncoderDecoderASR | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import random | |
| # Initialize psychometric model | |
| psych_model_name = "KevSun/Personality_LM" | |
| psych_model = pipeline("text-classification", model=psych_model_name) | |
| # Initialize ASR and TTS models | |
| asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-crdnn-rnnlm-librispeech", savedir="tmp_asr") | |
| 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") | |
| # Function to analyze text responses | |
| 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): | |
| # Check if the audio input is None | |
| if audio is None: | |
| return "No audio provided" | |
| # Process the audio if it's a valid input | |
| try: | |
| text_response = asr_model.transcribe_file(audio) | |
| return text_response | |
| except Exception as e: | |
| return f"Error processing audio: {str(e)}" | |
| # Function to generate dynamic questions based on answers | |
| def generate_dynamic_question(previous_answer): | |
| # Example of simple follow-up questions based on the answer | |
| if "teamwork" in previous_answer.lower(): | |
| return "Can you share a specific instance where you worked in a team?" | |
| elif "challenge" in previous_answer.lower(): | |
| return "How did you overcome that challenge? What steps did you take?" | |
| elif "stress" in previous_answer.lower(): | |
| return "How do you manage stress during high-pressure situations?" | |
| else: | |
| # Default follow-up question | |
| return "Can you tell me more about that?" | |
| # Gradio UI function to handle dynamic conversation | |
| def chat_interface(candidate_name, *responses): | |
| conversation_history = [] | |
| # Iterate through responses to generate follow-up questions | |
| for i, response in enumerate(responses): | |
| conversation_history.append(f"Q{i+1}: {response}") | |
| # Generate dynamic question based on the previous response | |
| dynamic_question = generate_dynamic_question(response) | |
| conversation_history.append(f"Follow-up Question: {dynamic_question}") | |
| # Process text responses | |
| text_df, text_plot = text_part(candidate_name, responses) | |
| # Process audio responses | |
| audio_df, audio_plot = audio_part(candidate_name, responses) | |
| # Return conversation history and analysis | |
| return "\n".join(conversation_history), text_df, text_plot, audio_df, audio_plot | |
| # Create text inputs and audio inputs | |
| text_inputs = [gr.Textbox(label=f"Response to Q{i+1}:") for i in range(5)] # Assuming we have up to 5 text responses | |
| audio_inputs = [gr.Audio(label=f"Response to Audio Q{i+1}:") for i in range(2)] # Assuming we have up to 2 audio responses | |
| interface = gr.Interface( | |
| fn=chat_interface, | |
| inputs=[gr.Textbox(label="Candidate Name")] + text_inputs + audio_inputs, | |
| outputs=["text", "dataframe", "plot", "dataframe", "plot"], | |
| title="Dynamic Psychometric Analysis Chatbot" | |
| ) | |
| # Launch the interface | |
| interface.launch() | |