import os os.environ["STREAMLIT_WATCHER_TYPE"] = "none" os.environ["PYTORCH_DISABLE_WIN_FIX"] = "1" import streamlit as st import json from datetime import datetime from core.utils import FileProcessor, star_rating from core.question_generator import generate_question from core.answering_competitor import Answering_competitor from core.response_evaluator import scorer from core.summary_utils import custom_css, generate_text_summary, clean_json_response from core.generate_summary import generate_summary_content from core.speech_converter import text_to_audio, load_model # Page configuration st.set_page_config( page_title="AI Job Matcher - HiredGPT Duel", page_icon="🤖", layout="wide" ) whisper_model = load_model() if 'questions_generated' not in st.session_state: st.session_state.questions_generated = [] if "page_stack" not in st.session_state: st.session_state.page_stack = ["Welcome"] if "track_score" not in st.session_state: st.session_state["track_score"] = [] if st.session_state.page_stack[-1] == "Welcome": st.title("🤖 Welcome to AI Job Matcher - HiredGPT Duel") st.markdown(""" ### 🎯 How it works: 1. **📄 Upload your resume** in the sidebar 2. **📋 Provide job description** (upload file or paste text) 3. **⭐ Set competitor strength** (how challenging should it be?) 4. **💼 Enter the job role** you're applying for 5. **🚀 Click submit** to start the duel! **Complete all fields in the sidebar to begin →** """) if st.session_state.page_stack[-1] == "Loading": st.title("🤖 Welcome to AI Job Matcher - HiredGPT Duel") st.success("🎉 All files processed successfully!") with st.spinner("🤖 Generating interview questions..."): st.session_state.questions = generate_question(st.session_state.resume_text, st.session_state.jd_text, st.session_state.job_role) with st.spinner("🔧 Initializing your interview competitor..."): comp_ans_gen = Answering_competitor(resume=st.session_state.resume_text, job_description=st.session_state.jd_text, difficulty_level=st.session_state.improve_percentage, questions=st.session_state.questions) comp_ans_gen.extract_factors() comp_ans_gen.determine_enhancement() comp_ans_gen.generate_resume() with st.spinner("🧠 Crafting rival candidate persona..."): comp_answers = comp_ans_gen.answer_questions() for key, val in comp_answers.items(): st.session_state[f"llm_answer_{key-1}"] = val st.session_state.page_stack.append("Ques_0") st.rerun() if st.session_state.page_stack[-1] == "Summary": st.markdown(custom_css, unsafe_allow_html=True) # Initialize lists to store answers user_answers = [] ai_answers = [] # Safely collect answers for ques in range(len(st.session_state.questions)): # Get user answer with fallback user_answer = st.session_state.get(f"user_answer_{ques}", "No answer provided") user_answers.append(user_answer) # Get AI answer with fallback ai_answer = st.session_state.get(f"llm_answer_{ques}", "No AI answer available") ai_answers.append(ai_answer) # Store in session state st.session_state.user_answers = user_answers st.session_state.ai_answers = ai_answers # Get scores safely st.session_state.scores = [ [ int(track_score["structure_star"]["score"]), int(track_score["depth"]["score"]), int(track_score["clarity"]["score"]), int(track_score["correctness"]["score"]) ] for track_score in st.session_state.track_score ] if st.session_state.get("track_score") else [] if "summary_data" not in st.session_state: with st.spinner("Analyzing your interview performance..."): try: summary_json = generate_summary_content( st.session_state.resume_text, st.session_state.jd_text, st.session_state.job_role, st.session_state.questions, st.session_state.user_answers, st.session_state.ai_answers, st.session_state.scores ) cleaned_json = clean_json_response(summary_json) try: summary_data = json.loads(cleaned_json) st.session_state.summary_data = summary_data except json.JSONDecodeError as e: st.error(f"Error parsing summary data: {e}") st.code(summary_json) # Show the raw response for debugging st.stop() except Exception as e: st.error(f"Error generating summary: {e}") st.stop() summary_data = st.session_state.summary_data for key in ["strengths", "weaknesses", "resources"]: if key in summary_data and isinstance(summary_data[key], list): summary_data[key] = "\n".join(summary_data[key]) if "topics_covered" in summary_data: # If topics is a string, split it into a list if isinstance(summary_data["topics_covered"], str): # Split by common separators if "," in summary_data["topics_covered"]: summary_data["topics_covered"] = [t.strip() for t in summary_data["topics_covered"].split(",")] elif ";" in summary_data["topics_covered"]: summary_data["topics_covered"] = [t.strip() for t in summary_data["topics_covered"].split(";")] elif "\n" in summary_data["topics_covered"]: summary_data["topics_covered"] = [t.strip() for t in summary_data["topics_covered"].split("\n")] # If no separators, use the whole string as one topic else: summary_data["topics_covered"] = [summary_data["topics_covered"]] scores_list = st.session_state.scores avg_score = sum([sum(q_score) for q_score in scores_list]) / (len(scores_list) * 4) * 10 text_summary = generate_text_summary(summary_data, scores_list) st.markdown('
', unsafe_allow_html=True) st.markdown('

Interview Performance Summary

', unsafe_allow_html=True) st.markdown(f'

Review your performance and get insights to improve your next interview.

', unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) # Center the download button using columns _, download_col, _ = st.columns([3, 4, 3]) with download_col: st.markdown('
', unsafe_allow_html=True) st.download_button( label="Download Summary", data=text_summary, file_name=f"interview_summary_{datetime.now().strftime('%Y%m%d_%H%M%S')}.txt", mime="text/plain", use_container_width=True # Make button take full column width ) st.markdown('
', unsafe_allow_html=True) left_col, right_col = st.columns([8, 2]) with right_col: st.markdown('
', unsafe_allow_html=True) # Overall Score st.markdown(f"""
{avg_score:.1f}/100
Overall Score
""", unsafe_allow_html=True) # Topics Covered topics = summary_data.get("topics_covered", []) st.markdown(f"""
{len(topics)}
Topics Covered
""", unsafe_allow_html=True) st.markdown(f"""
{len(summary_data.get("comparison_table", []))}
Questions Analyzed
""", unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) if topics: st.markdown('
', unsafe_allow_html=True) st.markdown('
Topics List
', unsafe_allow_html=True) # Fixed: Create a single string with all topic badges properly formatted topics_html = "" for topic in topics: if isinstance(topic, str): # Properly handle each topic as a full word topics_html += f'
{topic}
' # Display all topics as HTML st.markdown(topics_html, unsafe_allow_html=True) st.markdown('
', unsafe_allow_html=True) with left_col: # Display trends - removed background st.markdown('

Performance Overview

', unsafe_allow_html=True) st.info(summary_data.get("trends", "No trend data available")) # Add section divider st.markdown('
', unsafe_allow_html=True) # Strengths and weaknesses section - removed content wrapper strength_weak_cols = st.columns(2) with strength_weak_cols[0]: st.markdown('

Your Strengths

', unsafe_allow_html=True) st.markdown(f'
{summary_data.get("strengths", "No strengths data available")}
', unsafe_allow_html=True) with strength_weak_cols[1]: st.markdown('

Areas for Improvement

', unsafe_allow_html=True) st.markdown(f'
{summary_data.get("weaknesses", "No weaknesses data available")}
', unsafe_allow_html=True) # Add section divider st.markdown('
', unsafe_allow_html=True) # Question-by-question analysis - removed content wrapper st.markdown('

Question Analysis

', unsafe_allow_html=True) comparison_table = summary_data.get("comparison_table", []) for i, item in enumerate(comparison_table): with st.expander(f"Q{i+1}: {item.get('question', 'Question')}"): qa_cols = st.columns(2) with qa_cols[0]: st.markdown('

Key Differences

', unsafe_allow_html=True) st.info(item.get("differences", "No comparison available")) with qa_cols[1]: st.markdown('

Strong Phrases

', unsafe_allow_html=True) st.success(item.get("strong_phrases", "No notable phrases identified")) # Display answers side by side if i < len(st.session_state.user_answers) and i < len(st.session_state.ai_answers): tab1, tab2 = st.tabs(["Your Answer", "AI Answer"]) with tab1: st.markdown(st.session_state.user_answers[i]) with tab2: st.markdown(st.session_state.ai_answers[i]) # Add section divider st.markdown('
', unsafe_allow_html=True) # Resources section - make links blue, removed content wrapper st.markdown('

Recommended Resources

', unsafe_allow_html=True) # Format resources as a list with blue link styling resources = summary_data.get("resources", "No resources available") if isinstance(resources, str): if "\n" in resources: resources_list = resources.split("\n") resources_html = '' st.markdown(resources_html, unsafe_allow_html=True) else: # Single resource, no newlines st.markdown(f'
{resources}
', unsafe_allow_html=True) if st.button("Rerun"): st.rerun() if st.session_state.page_stack[-1].startswith("Ques_"): ques = int(st.session_state.page_stack[-1][-1]) st.header("🥊 Interview Duel") st.subheader(f"Question {ques+1}/{len(st.session_state.questions)}") st.info(st.session_state.questions[ques]) user_ans, llm_ans = st.columns(2) user_answer = "" with user_ans: st.markdown("**👤 Your Answer:**") if f"submitted_ans_{ques}" in st.session_state and st.session_state[f"submitted_ans_{ques}"]: st.audio_input(label="Record Audio",disabled=True, label_visibility="collapsed") st.text_area( "Type your answer here", value=st.session_state[f"user_answer_{ques}"], height=150, label_visibility="collapsed", disabled=True ) else: audio_file = st.audio_input(label="Record Audio",key=f"audio_ip_{ques}" ,label_visibility="collapsed") transcribed_text = "" if audio_file: file_path = os.path.join("audio",f"user_answer_{ques}.wav") try: with open(file_path, "wb") as f: audio_file.seek(0) f.write(audio_file.read()) transcribed_text = whisper_model.transcribe(file_path)["text"] # print(st.session_state[f"transcribed_{ques}"]) # st.rerun() except Exception as e: st.error(f"Error occured while transcribing {e}") user_answer = st.text_area( "Type your answer here", value= transcribed_text, height=150, label_visibility="collapsed", ) with llm_ans: st.markdown("**🤖 Rival's Answer:**") if f"submitted_ans_{ques}" in st.session_state and st.session_state[f"submitted_ans_{ques}"]: st.text_area("Competitor Response",st.session_state[f"llm_answer_{ques}"], height=150, label_visibility="collapsed") st.audio(data=os.path.join("audio",f"llm_answer_{ques}.wav")) else: # st.text_area("Competitor Response",value="Hidden values", height=150, label_visibility="collapsed") st.markdown(f"""
{st.session_state[f"llm_answer_{ques}"]}
""", unsafe_allow_html=True) try: os.makedirs("audio",exist_ok=True) text_to_audio(st.session_state[f"llm_answer_{ques}"], os.path.join("audio",f"llm_answer_{ques}.wav")) except Exception as e: print(e) st.error(f"An error occurred {e}") left_area,_ = st.columns([1,1]) _, submit_area, _ = left_area.columns([1,3,1]) with submit_area: if st.button("🚀 Submit & Compare Answers", use_container_width=True, type="primary"): st.session_state[f"submitted_ans_{ques}"] = True st.session_state[f"user_answer_{ques}"] = user_answer print("User answer\n",st.session_state[f"user_answer_{ques}"]) st.rerun() if f"submitted_ans_{ques}" in st.session_state and st.session_state[f"submitted_ans_{ques}"]: result_score = dict() if f"result_score_{ques}" in st.session_state and st.session_state[f"result_score_{ques}"]: result_score = st.session_state[f"result_score_{ques}"] else: result_score = scorer( jd= st.session_state.jd_text, ques= st.session_state.questions[ques], user= st.session_state[f"user_answer_{ques}"], competitor= st.session_state[f"llm_answer_{ques}"], ) st.session_state[f"result_score_{ques}"] = result_score st.session_state.track_score.append(result_score["user"]) with st.container(border=True): user_score, llm_score = st.columns(2) with user_score: participant = result_score["user"] st.markdown(f"**Structure:** {star_rating(participant['structure_star']['score'])}") st.markdown(f"**Depth:** {star_rating(participant['depth']['score'])}") st.markdown(f"**Clarity:** {star_rating(participant['clarity']['score'])}") st.markdown(f"**Correctness:** {star_rating(participant['correctness']['score'])}") with llm_score: participant = result_score["competitor"] st.markdown(f"**Structure:** {star_rating(participant['structure_star']['score'])}") st.markdown(f"**Depth:** {star_rating(participant['depth']['score'])}") st.markdown(f"**Clarity:** {star_rating(participant['clarity']['score'])}") st.markdown(f"**Correctness:** {star_rating(participant['correctness']['score'])}") st.markdown("---") prev,_,next = st.columns([1,3,1]) if ques>0: with prev: if st.button("⬅️ Previous Question"): st.session_state.page_stack.pop() st.rerun() with next: if ques < len(st.session_state.questions)-1: if st.button("Next Question ➡️"): st.session_state.page_stack.append(f"Ques_{ques+1}") st.rerun() if ques == len(st.session_state.questions) -1: if st.button("📊 Summary"): st.session_state.page_stack.append("Summary") st.rerun() with st.sidebar: st.title("AI-Job_Matcher - HiredGPT Duel") st.markdown("---") # 1. Resume Upload st.header("📄 1. Upload Your Resume") user_resume = st.file_uploader("Upload your resume in PDF", type=['pdf']) # 2. Job Description st.header("📋 2. Upload or Paste Job Description") job_desc_method = st.radio("How would you like to provide the job description?", options=["Upload file", "Paste text"]) if job_desc_method == "Upload file": job_desc_file = st.file_uploader("Upload job description (PDF, TXT)", type=['pdf', 'txt'], key="jd") job_desc_text = None else: job_desc_file = None job_desc_text = st.text_area("Paste the job description text here") # 3. Star Competitor Settings st.header("⭐ 3. Star Competitor Settings") improve_percentage = st.slider("How much stronger should the rival be? (%)", min_value=10, max_value=100, value=10, step=10) st.info(f"🎯 Your competitor will be {improve_percentage}% stronger") # 4. Job Role/Title st.header("💼 4. Job Role/Title") job_role = st.text_input("Job role/title you're applying for") st.markdown("---") submit_button = st.button("🚀 Start HiredGPT Duel", type="primary", use_container_width=True) if submit_button: # Clear previous session state to restart the process if 'resume_text' in st.session_state: del st.session_state.resume_text if 'jd_text' in st.session_state: del st.session_state.jd_text if 'questions_generated' in st.session_state: st.session_state.questions_generated = [] if "page_stack" in st.session_state: st.session_state.page_stack = ["Welcome"] missing_fields = [] if not user_resume: missing_fields.append("📄 Resume") if job_desc_method == "Upload file": if not job_desc_file: missing_fields.append("📋 Job Description File") else: if not job_desc_text or len(job_desc_text.strip()) < 10: missing_fields.append("📋 Job Description Text") if not job_role or len(job_role.strip()) == 0: missing_fields.append("💼 Job Role/Title") if missing_fields: st.error(f"❌ Please complete the following fields:\n\n" + "\n\n".join([f"• {field}" for field in missing_fields])) else: st.success("✅ All fields completed successfully!") st.session_state.improve_percentage = improve_percentage st.session_state.job_role = job_role resume_text = "" try: resume_text = FileProcessor.read_resume(user_resume) with st.sidebar: st.success("✅ Resume uploaded successfully!") except Exception as e: with st.sidebar: st.error(f"❌ Error reading resume: {e}") st.stop() jd_text = '' if job_desc_file: try: if job_desc_file.type == "application/pdf": jd_text = FileProcessor.read_job_description_pdf(job_desc_file) elif job_desc_file.type == "text/plain": jd_text = FileProcessor.read_job_description_txt(job_desc_file) else: st.error("Unsupported file type for job description.") st.stop() with st.sidebar: st.success("✅ Job description uploaded successfully!") except Exception as e: with st.sidebar: st.error(f"❌ Error reading job description: {e}") st.stop() elif job_desc_text: jd_text = job_desc_text.strip() with st.sidebar: st.success("✅ Job description text processed!") if resume_text and jd_text: st.session_state.resume_text = resume_text st.session_state.jd_text = jd_text st.session_state.page_stack.append("Loading") st.rerun()