app.py

import os
os.system("pip install --upgrade openai-whisper torch")
os.system("pip install --upgrade transformers")
import whisper
import gradio as gr
import torch
from transformers import BertTokenizer, BertForSequenceClassification
from app.questions import get_question

# Load Whisper model
whisper_model = whisper.load_model("small")

# Load the pre-trained model and tokenizer for confidence analysis
confidence_model = BertForSequenceClassification.from_pretrained('RiteshAkhade/Confidence_model3')
confidence_tokenizer = BertTokenizer.from_pretrained('RiteshAkhade/Confidence_model3')

# Load pre-trained context analysis model (BERT-based)
context_model = BertForSequenceClassification.from_pretrained('RiteshAkhade/context_model')
context_tokenizer = BertTokenizer.from_pretrained('RiteshAkhade/context_model')

# Function to perform context analysis using the BERT model
def predict_relevance(question, answer):
    if not answer.strip():  # Check for empty answers
        return "Irrelevant"
    
    inputs = context_tokenizer(question, answer, return_tensors="pt", padding=True, truncation=True)
    context_model.eval()

    with torch.no_grad():
        outputs = context_model(**inputs)
        logits = outputs.logits
        probabilities = torch.softmax(logits, dim=-1)
        threshold = 0.5
        relevant_prob = probabilities[0, 1]  # Probability for relevant class
    
    if relevant_prob > threshold:
        return "Relevant"
    else:
        return "Irrelevant"

#  confidence analysis
def predict_confidence(question, answer):
    if not answer.strip():  
        return "Not Confident"

    inputs = confidence_tokenizer(question, answer, return_tensors="pt", padding=True, truncation=True)
    confidence_model.eval()

    with torch.no_grad():
        outputs = confidence_model(**inputs)
        logits = outputs.logits
        probabilities = torch.softmax(logits, dim=-1)
        
        print("Logits:", logits)
        print("Probabilities:", probabilities)
        
        confidence_prob = probabilities[0, 1]  # Probability for Confident class

        #  lower threshold
        return "Confident" if confidence_prob > 0.475 else "Not Confident"

# Questions from questions.py
def fetch_questions():
    return [get_question(i) for i in range(6)]

questions = fetch_questions()
current_question_index = 0

# Function to show the current question
def show_question():
    global current_question_index
    question = questions[current_question_index]
    return question

# Function to move to the next question
def next_question():
    global current_question_index
    current_question_index = (current_question_index + 1) % len(questions)
    return show_question(), None, "", "", ""

# Function to transcribe the audio input and perform both context and confidence analyses
def transcribe_and_analyze(audio, question):
    try:
        # Load and process audio using Whisper
        audio = whisper.load_audio(audio)
        audio = whisper.pad_or_trim(audio)
        mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device)
        options = whisper.DecodingOptions(fp16=False)
        result = whisper.decode(whisper_model, mel, options)
        
        # Get the transcribed text
        transcribed_text = result.text

        # Perform context and confidence analysis
        context_result = predict_relevance(question, transcribed_text)
        confidence_result = predict_confidence(question, transcribed_text)

        # Return the results
        return transcribed_text, context_result, confidence_result
    except Exception as e:
        return f"Error: {str(e)}", "", ""

#  Gradio interface 
with gr.Blocks() as demo:
    
    gr.HTML('''
    <style>
        body { 
            background-color: #f0f0f0; 
        }
        #title { 
            color: grey; 
            font-size: 30px; 
            text-align: center; 
            margin-bottom: 20px; 
        }
        .transcribe-btn, .next-btn { 
            background-color: #4CAF50; 
            color: white; 
            font-size: 16px; 
            padding: 10px 20px; 
            border-radius: 5px; 
            cursor: pointer; 
            margin-top: 10px;
        }
        .transcribe-btn:hover, .next-btn:hover { 
            background-color: #45a049; 
        }
        #question-box { 
            font-size: 20px; 
            color: #555; 
            text-align: center; 
        }
        #text-box {
            font-size: 18px;
            color: #333;
        }
        #context-box, #confidence-box {
            font-size: 18px;
            color: #333;
        }
    </style>
    ''')

    # Title
    gr.Markdown("<h1 id='title'>INTERVIEW PREPARATION MODEL</h1>")

    # Question display
    with gr.Row():
        question_display = gr.Textbox(label="Interview Question", value=show_question(), interactive=False, elem_id="question-box")

    # Audio input and transcription section
    with gr.Row():
        audio_input = gr.Audio(type="filepath", label="Record Your Answer")

    # Separate text boxes for the transcribed text, context, and confidence analysis
    with gr.Row():
        transcribed_text = gr.Textbox(label="Your Answer (Transcription)", interactive=False, lines=5, elem_id="text-box")
    
    with gr.Row():
        context_analysis_result = gr.Textbox(label="Context Analysis", interactive=False, elem_id="context-box")
    
    with gr.Row():
        confidence_analysis_result = gr.Textbox(label="Confidence Analysis", interactive=False, elem_id="confidence-box")
    
    # Automatically transcribe the audio and analyze context and confidence when audio is provided
    audio_input.change(fn=transcribe_and_analyze, 
                       inputs=[audio_input, question_display], 
                       outputs=[transcribed_text, context_analysis_result, confidence_analysis_result])

    # Button to get the next question
    with gr.Row():    
        next_button = gr.Button("Next Question", elem_classes="next-btn")
        
        # Clear audio, transcription, context, and confidence when moving to the next question
        next_button.click(next_question, 
                          outputs=[question_display, audio_input, transcribed_text, context_analysis_result, confidence_analysis_result])

# Launch app
demo.launch(share=True)