app.py

import time
import os
import queue
import sounddevice as sd
import numpy as np
import requests
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import json
import PyPDF2

# Hugging Face API endpoint and keys
HF_API_URL_STT = "https://api-inference.huggingface.co/models/openai/whisper-base"
HF_API_URL_CONVERSATION = "https://api-inference.huggingface.co/models/facebook/blenderbot-400M-distill"
HF_API_KEY = os.getenv('HF_API_KEY')  # Replace with your Hugging Face API key

# Parameters
silence_threshold = 0.01  # Silence threshold for audio detection
silence_duration = 2.0  # Duration of silence to detect end of speech
sample_rate = 16000  # Audio sample rate

# Audio buffer
audio_queue = queue.Queue()

# Load a pre-trained model for vector embeddings
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

# Parse PDF and create resume content
def parse_resume(pdf_path):
    """Extract text from a PDF file."""
    try:
        with open(pdf_path, 'rb') as f:
            reader = PyPDF2.PdfReader(f)
            text = "\n".join(page.extract_text() for page in reader.pages if page.extract_text())
        sections = {"Resume Content": text}
        return sections
    except Exception as e:
        print(f"Error reading PDF: {e}")
        return {}

# Load vector database (resume content)
def load_resume(pdf_path):
    resume_content = parse_resume(pdf_path)
    resume_embeddings = {
        section: embedding_model.encode(content) for section, content in resume_content.items()
    }
    return resume_embeddings

def get_relevant_question(user_input, resume_embeddings):
    """Find the most relevant section in the resume and generate a question."""
    user_embedding = embedding_model.encode(user_input)
    similarities = {
        section: cosine_similarity([user_embedding], [embedding])[0][0] 
        for section, embedding in resume_embeddings.items()
    }
    most_relevant_section = max(similarities, key=similarities.get)
    return f"Based on your experience in {most_relevant_section}, can you elaborate more?"

def audio_callback(indata, frames, time, status):
    """Audio callback to store audio in the queue."""
    if status:
        print(f"Audio error: {status}")
    audio_queue.put(indata.copy())

def record_audio():
    """Record audio and stop on silence detection."""
    print("Recording. Speak now!")
    buffer = []
    silence_start = None

    with sd.InputStream(samplerate=sample_rate, channels=1, callback=audio_callback):
        while True:
            try:
                # Fetch audio data
                data = audio_queue.get()
                buffer.append(data)

                # Check for silence
                rms = np.sqrt(np.mean(data**2))
                if rms < silence_threshold:
                    if silence_start is None:
                        silence_start = time.time()
                    elif time.time() - silence_start > silence_duration:
                        print("Silence detected. Stopping recording.")
                        break
                else:
                    silence_start = None

            except KeyboardInterrupt:
                print("Recording stopped by user.")
                break

    audio_data = np.concatenate(buffer, axis=0)
    return audio_data

def transcribe_audio(audio_data):
    """Transcribe audio to text using Hugging Face Whisper API."""
    print("Transcribing audio...")
    headers = {"Authorization": f"Bearer {HF_API_KEY}"}
    response = requests.post(
        HF_API_URL_STT,
        headers=headers,
        data=audio_data.tobytes(),
    )
    if response.status_code == 200:
        return response.json().get("text", "")
    else:
        print(f"Error: {response.status_code} {response.text}")
        return ""

def generate_question(response, resume_embeddings):
    """Generate a question based on the user's response using Hugging Face API."""
    if resume_embeddings:
        return get_relevant_question(response, resume_embeddings)
    
    print("Generating a question...")
    headers = {"Authorization": f"Bearer {HF_API_KEY}"}
    payload = {"inputs": {"past_user_inputs": [""], "generated_responses": [""], "text": response}}
    response = requests.post(
        HF_API_URL_CONVERSATION,
        headers=headers,
        json=payload
    )
    if response.status_code == 200:
        return response.json().get("generated_text", "Could you elaborate on that?")
    else:
        print(f"Error: {response.status_code} {response.text}")
        return "Sorry, I couldn't generate a question."

def main():
    print("Mock Interview System Initialized")

    # Load the resume embeddings
    pdf_path = "resume.pdf"  # Replace with the path to your PDF resume file
    if os.path.exists(pdf_path):
        print("Loading resume...")
        resume_embeddings = load_resume(pdf_path)
    else:
        print("Resume file not found. Proceeding without it.")
        resume_embeddings = None

    while True:
        try:
            # Record audio
            audio_data = record_audio()

            # Transcribe to text
            response = transcribe_audio(audio_data)
            print(f"You said: {response}")

            # Generate and ask the next question
            question = generate_question(response, resume_embeddings)
            print(f"Interview AI: {question}")

        except Exception as e:
            print(f"Error: {e}")
            break

if __name__ == "__main__":
    main()