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| 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.") | |