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| import streamlit as st | |
| import os | |
| import logging | |
| import dotenv | |
| import yaml | |
| import PyPDF2 | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_community.embeddings import HuggingFaceEmbeddings | |
| from langchain.prompts import PromptTemplate | |
| from langchain.llms import HuggingFaceHub | |
| import os | |
| dotenv.load_dotenv() | |
| # Load configuration from YAML | |
| def load_config(): | |
| with open("config.yaml", "r") as f: | |
| return yaml.safe_load(f) | |
| config = load_config() | |
| hf_token = os.getenv("Gem") # Store API token in .env | |
| logging.basicConfig(level=logging.INFO) | |
| # Load embedding model | |
| embeddings_model = HuggingFaceEmbeddings(model_name=config["embedding_model"]) | |
| # Extract text from PDFs | |
| def extract_text_from_pdf(file): | |
| reader = PyPDF2.PdfReader(file) | |
| text = "" | |
| for page in reader.pages: | |
| text += page.extract_text() or "" | |
| return text.strip() | |
| # Get interview questions and assess responses | |
| def get_interview_response(jd_text, resume_text, candidate_response=None): | |
| prompt_template = """ | |
| You are an AI interviewer assessing a candidate for a job role. | |
| JOB DESCRIPTION: | |
| {jd_text} | |
| CANDIDATE PROFILE: | |
| {resume_text} | |
| 1. Start by asking an **introductory question**: "Tell me about yourself." | |
| 2. Then, based on the job description, ask a **technical question**. | |
| 3. If the candidate has already responded, evaluate their answer and provide constructive feedback. | |
| Maintain a professional yet friendly tone. | |
| """ | |
| prompt = PromptTemplate( | |
| input_variables=["jd_text", "resume_text"], | |
| template=prompt_template | |
| ).format(jd_text=jd_text, resume_text=resume_text) | |
| if candidate_response: | |
| prompt += f"\n\nCANDIDATE RESPONSE: {candidate_response}\n\nAssess the response and provide feedback." | |
| llm = HuggingFaceHub( | |
| repo_id=config["model_name"], | |
| model_kwargs={"temperature": config["temperature"], "max_length": 200}, | |
| huggingfacehub_api_token=hf_token | |
| ) | |
| return llm(prompt).strip() | |
| # Streamlit UI | |
| st.set_page_config(page_title="AI Interviewer", layout="centered") | |
| st.title("🤖 AI Interview Chatbot") | |
| st.write("Upload a Job Description and Resume to start the interview.") | |
| jd_file = st.file_uploader("Upload Job Description (PDF)", type=["pdf"]) | |
| resume_file = st.file_uploader("Upload Candidate Resume (PDF)", type=["pdf"]) | |
| if jd_file and resume_file: | |
| jd_text = extract_text_from_pdf(jd_file) | |
| resume_text = extract_text_from_pdf(resume_file) | |
| if "interview_history" not in st.session_state: | |
| st.session_state["interview_history"] = [] | |
| first_question = get_interview_response(jd_text, resume_text) | |
| st.session_state["interview_history"].append(("AI", first_question)) | |
| for role, msg in st.session_state["interview_history"]: | |
| st.chat_message(role).write(msg) | |
| query = st.chat_input("Your Response:") | |
| if query: | |
| response = get_interview_response(jd_text, resume_text, query) | |
| st.session_state["interview_history"].append(("You", query)) | |
| st.session_state["interview_history"].append(("AI", response)) | |
| st.rerun() |