Upload 3 files

Behavioral Screen.py

@@ -0,0 +1,232 @@
+import streamlit as st
+from streamlit_lottie import st_lottie
+from typing import Literal
+from dataclasses import dataclass
+import json
+import base64
+from langchain.memory import ConversationBufferMemory
+from langchain.callbacks import get_openai_callback
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationChain, RetrievalQA
+from langchain.prompts.prompt import PromptTemplate
+from langchain.text_splitter import NLTKTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+import nltk
+from prompts.prompts import templates
+# Audio
+from speech_recognition.openai_whisper import save_wav_file, transcribe
+from audio_recorder_streamlit import audio_recorder
+from aws.synthesize_speech import synthesize_speech
+from IPython.display import Audio
+
+def load_lottiefile(filepath: str):
+
+    '''Load lottie animation file'''
+
+    with open(filepath, "r") as f:
+        return json.load(f)
+
+st_lottie(load_lottiefile("images/welcome.json"), speed=1, reverse=False, loop=True, quality="high", height=300)
+
+#st.markdown("""solutions to potential errors:""")
+with st.expander("""Why did I encounter errors when I tried to talk to the AI Interviewer?"""):
+    st.write("""
+    This is because the app failed to record. Make sure that your microphone is connected and that you have given permission to the browser to access your microphone.""")
+
+st.markdown("""\n""")
+jd = st.text_area("""Please enter the job description here (If you don't have one, enter keywords, such as "communication" or "teamwork" instead): """)
+auto_play = st.checkbox("Let AI interviewer speak! (Please don't switch during the interview)")
+#st.toast("4097 tokens is roughly equivalent to around 800 to 1000 words or 3 minutes of speech. Please keep your answer within this limit.")
+
+@dataclass
+class Message:
+    '''dataclass for keeping track of the messages'''
+    origin: Literal["human", "ai"]
+    message: str
+
+def autoplay_audio(file_path: str):
+    '''Play audio automatically'''
+    def update_audio():
+        global global_audio_md
+        with open(file_path, "rb") as f:
+            data = f.read()
+            b64 = base64.b64encode(data).decode()
+            global_audio_md = f"""
+                <audio controls autoplay="true">
+                <source src="data:audio/mp3;base64,{b64}" type="audio/mp3">
+                </audio>
+                """
+    def update_markdown(audio_md):
+        st.markdown(audio_md, unsafe_allow_html=True)
+    update_audio()
+    update_markdown(global_audio_md)
+
+def embeddings(text: str):
+
+    '''Create embeddings for the job description'''
+
+    nltk.download('punkt')
+    text_splitter = NLTKTextSplitter()
+    texts = text_splitter.split_text(text)
+    # Create emebeddings
+    embeddings = OpenAIEmbeddings()
+    docsearch = FAISS.from_texts(texts, embeddings)
+    retriever = docsearch.as_retriever(search_tupe='similarity search')
+    return retriever
+
+def initialize_session_state():
+
+    '''Initialize session state variables'''
+
+    if "retriever" not in st.session_state:
+        st.session_state.retriever = embeddings(jd)
+    if "chain_type_kwargs" not in st.session_state:
+        Behavioral_Prompt = PromptTemplate(input_variables=["context", "question"],
+                                          template=templates.behavioral_template)
+        st.session_state.chain_type_kwargs = {"prompt": Behavioral_Prompt}
+    # interview history
+    if "history" not in st.session_state:
+        st.session_state.history = []
+        st.session_state.history.append(Message("ai", "Hello there! I am your interviewer today. I will access your soft skills through a series of questions. Let's get started! Please start by saying hello or introducing yourself. Note: The maximum length of your answer is 4097 tokens!"))
+    # token count
+    if "token_count" not in st.session_state:
+        st.session_state.token_count = 0
+    if "memory" not in st.session_state:
+        st.session_state.memory = ConversationBufferMemory()
+    if "guideline" not in st.session_state:
+        llm = ChatOpenAI(
+            model_name="gpt-3.5-turbo",
+            temperature=0.8, )
+        st.session_state.guideline = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type_kwargs=st.session_state.chain_type_kwargs, chain_type='stuff',
+            retriever=st.session_state.retriever, memory=st.session_state.memory).run(
+            "Create an interview guideline and prepare total of 8 questions. Make sure the questions tests the soft skills")
+    # llm chain and memory
+    if "conversation" not in st.session_state:
+        llm = ChatOpenAI(
+        model_name = "gpt-3.5-turbo",
+        temperature = 0.8,)
+        PROMPT = PromptTemplate(
+            input_variables=["history", "input"],
+            template="""I want you to act as an interviewer strictly following the guideline in the current conversation.
+                            Candidate has no idea what the guideline is.
+                            Ask me questions and wait for my answers. Do not write explanations.
+                            Ask question like a real person, only one question at a time.
+                            Do not ask the same question.
+                            Do not repeat the question.
+                            Do ask follow-up questions if necessary. 
+                            You name is GPTInterviewer.
+                            I want you to only reply as an interviewer.
+                            Do not write all the conversation at once.
+                            If there is an error, point it out.
+
+                            Current Conversation:
+                            {history}
+
+                            Candidate: {input}
+                            AI: """)
+        st.session_state.conversation = ConversationChain(prompt=PROMPT, llm=llm,
+                                                       memory=st.session_state.memory)
+    if "feedback" not in st.session_state:
+        llm = ChatOpenAI(
+        model_name = "gpt-3.5-turbo",
+        temperature = 0.5,)
+        st.session_state.feedback = ConversationChain(
+            prompt=PromptTemplate(input_variables = ["history", "input"], template = templates.feedback_template),
+            llm=llm,
+            memory = st.session_state.memory,
+        )
+
+def answer_call_back():
+
+    '''callback function for answering user input'''
+
+    with get_openai_callback() as cb:
+        # user input
+        human_answer = st.session_state.answer
+        # transcribe audio
+        if voice:
+            save_wav_file("temp/audio.wav", human_answer)
+            try:
+                input = transcribe("temp/audio.wav")
+                # save human_answer to history
+            except:
+                st.session_state.history.append(Message("ai", "Sorry, I didn't get that."))
+                return "Please try again."
+        else:
+            input = human_answer
+
+        st.session_state.history.append(
+            Message("human", input)
+        )
+        # OpenAI answer and save to history
+        llm_answer = st.session_state.conversation.run(input)
+        # speech synthesis and speak out
+        audio_file_path = synthesize_speech(llm_answer)
+        # create audio widget with autoplay
+        audio_widget = Audio(audio_file_path, autoplay=True)
+        # save audio data to history
+        st.session_state.history.append(
+            Message("ai", llm_answer)
+        )
+        st.session_state.token_count += cb.total_tokens
+        return audio_widget
+
+### ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
+if jd:
+
+    initialize_session_state()
+    credit_card_placeholder = st.empty()
+    col1, col2 = st.columns(2)
+    with col1:
+        feedback = st.button("Get Interview Feedback")
+    with col2:
+        guideline = st.button("Show me interview guideline!")
+    audio = None
+    chat_placeholder = st.container()
+    answer_placeholder = st.container()
+
+    if guideline:
+        st.write(st.session_state.guideline)
+    if feedback:
+        evaluation = st.session_state.feedback.run("please give evalution regarding the interview")
+        st.markdown(evaluation)
+        st.download_button(label="Download Interview Feedback", data=evaluation, file_name="interview_feedback.txt")
+        st.stop()
+    else:
+        with answer_placeholder:
+            voice: bool = st.checkbox("I would like to speak with AI Interviewer!")
+            if voice:
+                answer = audio_recorder(pause_threshold=2.5, sample_rate=44100)
+                #st.warning("An UnboundLocalError will occur if the microphone fails to record.")
+            else:
+                answer = st.chat_input("Your answer")
+            if answer:
+                st.session_state['answer'] = answer
+                audio = answer_call_back()
+        with chat_placeholder:
+            for answer in st.session_state.history:
+                if answer.origin == 'ai':
+                    if auto_play and audio:
+                        with st.chat_message("assistant"):
+                            st.write(answer.message)
+                            st.write(audio)
+                    else:
+                        with st.chat_message("assistant"):
+                            st.write(answer.message)
+                else:
+                    with st.chat_message("user"):
+                        st.write(answer.message)
+
+        credit_card_placeholder.caption(f"""
+                        Progress: {int(len(st.session_state.history) / 30 * 100)}% completed.
+        """)
+
+else:
+    st.info("Please submit job description to start interview.")
+
+
+
+

Professional Screen.py

@@ -0,0 +1,203 @@
+import streamlit as st
+from streamlit_lottie import st_lottie
+from typing import Literal
+from dataclasses import dataclass
+import json
+import base64
+from langchain.memory import ConversationBufferMemory
+from langchain.callbacks import get_openai_callback
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationChain, RetrievalQA
+from langchain.prompts.prompt import PromptTemplate
+from langchain.text_splitter import NLTKTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+import nltk
+from prompts.prompts import templates
+# Audio
+from speech_recognition.openai_whisper import save_wav_file, transcribe
+from audio_recorder_streamlit import audio_recorder
+from aws.synthesize_speech import synthesize_speech
+from IPython.display import Audio
+
+
+def load_lottiefile(filepath: str):
+    with open(filepath, "r") as f:
+        return json.load(f)
+st_lottie(load_lottiefile("images/welcome.json"), speed=1, reverse=False, loop=True, quality="high", height=300)
+
+#st.markdown("""solutions to potential errors:""")
+with st.expander("""Why did I encounter errors when I tried to talk to the AI Interviewer?"""):
+    st.write("""
+    This is because the app failed to record. Make sure that your microphone is connected and that you have given permission to the browser to access your microphone.""")
+
+jd = st.text_area("Please enter the job description here (If you don't have one, enter keywords, such as PostgreSQL or Python instead): ")
+auto_play = st.checkbox("Let AI interviewer speak! (Please don't switch during the interview)")
+
+#st.toast("4097 tokens is roughly equivalent to around 800 to 1000 words or 3 minutes of speech. Please keep your answer within this limit.")
+
+@dataclass
+class Message:
+    """class for keeping track of interview history."""
+    origin: Literal["human", "ai"]
+    message: str
+
+def save_vector(text):
+    """embeddings"""
+
+    nltk.download('punkt')
+    text_splitter = NLTKTextSplitter()
+    texts = text_splitter.split_text(text)
+     # Create emebeddings
+    embeddings = OpenAIEmbeddings()
+    docsearch = FAISS.from_texts(texts, embeddings)
+    return docsearch
+
+def initialize_session_state_jd():
+    """ initialize session states """
+    if 'jd_docsearch' not in st.session_state:
+        st.session_state.jd_docserch = save_vector(jd)
+    if 'jd_retriever' not in st.session_state:
+        st.session_state.jd_retriever = st.session_state.jd_docserch.as_retriever(search_type="similarity")
+    if 'jd_chain_type_kwargs' not in st.session_state:
+        Interview_Prompt = PromptTemplate(input_variables=["context", "question"],
+                                          template=templates.jd_template)
+        st.session_state.jd_chain_type_kwargs = {"prompt": Interview_Prompt}
+    if 'jd_memory' not in st.session_state:
+        st.session_state.jd_memory = ConversationBufferMemory()
+    # interview history
+    if "jd_history" not in st.session_state:
+        st.session_state.jd_history = []
+        st.session_state.jd_history.append(Message("ai",
+                                                   "Hello, Welcome to the interview. I am your interviewer today. I will ask you professional questions regarding the job description you submitted."
+                                                   "Please start by introducting a little bit about yourself. Note: The maximum length of your answer is 4097 tokens!"))
+    # token count
+    if "token_count" not in st.session_state:
+        st.session_state.token_count = 0
+    if "jd_guideline" not in st.session_state:
+        llm = ChatOpenAI(
+        model_name = "gpt-3.5-turbo",
+        temperature = 0.8,)
+        st.session_state.jd_guideline = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type_kwargs=st.session_state.jd_chain_type_kwargs, chain_type='stuff',
+            retriever=st.session_state.jd_retriever, memory = st.session_state.jd_memory).run("Create an interview guideline and prepare only one questions for each topic. Make sure the questions tests the technical knowledge")
+    # llm chain and memory
+    if "jd_screen" not in st.session_state:
+        llm = ChatOpenAI(
+            model_name="gpt-3.5-turbo",
+            temperature=0.8, )
+        PROMPT = PromptTemplate(
+            input_variables=["history", "input"],
+            template="""I want you to act as an interviewer strictly following the guideline in the current conversation.
+                            Candidate has no idea what the guideline is.
+                            Ask me questions and wait for my answers. Do not write explanations.
+                            Ask question like a real person, only one question at a time.
+                            Do not ask the same question.
+                            Do not repeat the question.
+                            Do ask follow-up questions if necessary. 
+                            You name is GPTInterviewer.
+                            I want you to only reply as an interviewer.
+                            Do not write all the conversation at once.
+                            If there is an error, point it out.
+
+                            Current Conversation:
+                            {history}
+
+                            Candidate: {input}
+                            AI: """)
+
+        st.session_state.jd_screen = ConversationChain(prompt=PROMPT, llm=llm,
+                                                           memory=st.session_state.jd_memory)
+    if 'jd_feedback' not in st.session_state:
+        llm = ChatOpenAI(
+            model_name="gpt-3.5-turbo",
+            temperature=0.8, )
+        st.session_state.jd_feedback = ConversationChain(
+            prompt=PromptTemplate(input_variables=["history", "input"], template=templates.feedback_template),
+            llm=llm,
+            memory=st.session_state.jd_memory,
+        )
+
+def answer_call_back():
+    with get_openai_callback() as cb:
+        # user input
+        human_answer = st.session_state.answer
+        # transcribe audio
+        if voice:
+            save_wav_file("temp/audio.wav", human_answer)
+            try:
+                input = transcribe("temp/audio.wav")
+                # save human_answer to history
+            except:
+                st.session_state.jd_history.append(Message("ai", "Sorry, I didn't get that."))
+                return "Please try again."
+        else:
+            input = human_answer
+
+        st.session_state.jd_history.append(
+            Message("human", input)
+        )
+        # OpenAI answer and save to history
+        llm_answer = st.session_state.jd_screen.run(input)
+        # speech synthesis and speak out
+        audio_file_path = synthesize_speech(llm_answer)
+        # create audio widget with autoplay
+        audio_widget = Audio(audio_file_path, autoplay=True)
+        # save audio data to history
+        st.session_state.jd_history.append(
+            Message("ai", llm_answer)
+        )
+        st.session_state.token_count += cb.total_tokens
+        return audio_widget
+
+if jd:
+    # initialize session states
+    initialize_session_state_jd()
+    #st.write(st.session_state.jd_guideline)
+    credit_card_placeholder = st.empty()
+    col1, col2 = st.columns(2)
+    with col1:
+        feedback = st.button("Get Interview Feedback")
+    with col2:
+        guideline = st.button("Show me interview guideline!")
+    chat_placeholder = st.container()
+    answer_placeholder = st.container()
+    audio = None
+    # if submit email adress, get interview feedback imediately
+    if guideline:
+        st.write(st.session_state.jd_guideline)
+    if feedback:
+        evaluation = st.session_state.jd_feedback.run("please give evalution regarding the interview")
+        st.markdown(evaluation)
+        st.download_button(label="Download Interview Feedback", data=evaluation, file_name="interview_feedback.txt")
+        st.stop()
+    else:
+        with answer_placeholder:
+            voice: bool = st.checkbox("I would like to speak with AI Interviewer")
+            if voice:
+                answer = audio_recorder(pause_threshold = 2.5, sample_rate = 44100)
+                #st.warning("An UnboundLocalError will occur if the microphone fails to record.")
+            else:
+                answer = st.chat_input("Your answer")
+            if answer:
+                st.session_state['answer'] = answer
+                audio = answer_call_back()
+        with chat_placeholder:
+            for answer in st.session_state.jd_history:
+                if answer.origin == 'ai':
+                    if auto_play and audio:
+                        with st.chat_message("assistant"):
+                            st.write(answer.message)
+                            st.write(audio)
+                    else:
+                        with st.chat_message("assistant"):
+                            st.write(answer.message)
+                else:
+                    with st.chat_message("user"):
+                        st.write(answer.message)
+
+        credit_card_placeholder.caption(f"""
+        Progress: {int(len(st.session_state.jd_history) / 30 * 100)}% completed.""")
+else:
+    st.info("Please submit a job description to start the interview.")

Resume Screen.py

@@ -0,0 +1,212 @@
+# langchain: https://python.langchain.com/
+from dataclasses import dataclass
+import streamlit as st
+from speech_recognition.openai_whisper import save_wav_file, transcribe
+from audio_recorder_streamlit import audio_recorder
+from langchain.callbacks import get_openai_callback
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import RetrievalQA, ConversationChain
+from langchain.prompts.prompt import PromptTemplate
+from prompts.prompts import templates
+from typing import Literal
+from aws.synthesize_speech import synthesize_speech
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.text_splitter import NLTKTextSplitter
+from PyPDF2 import PdfReader
+from prompts.prompt_selector import prompt_sector
+from streamlit_lottie import st_lottie
+import json
+from IPython.display import Audio
+import nltk
+
+
+def load_lottiefile(filepath: str):
+    with open(filepath, "r") as f:
+        return json.load(f)
+st_lottie(load_lottiefile("images/welcome.json"), speed=1, reverse=False, loop=True, quality="high", height=300)
+
+#st.markdown("""solutions to potential errors:""")
+with st.expander("""Why did I encounter errors when I tried to talk to the AI Interviewer?"""):
+    st.write("""This is because the app failed to record. Make sure that your microphone is connected and that you have given permission to the browser to access your microphone.""")
+with st.expander("""Why did I encounter errors when I tried to upload my resume?"""):
+    st.write("""
+    Please make sure your resume is in pdf format. More formats will be supported in the future.
+    """)
+
+st.markdown("""\n""")
+position = st.selectbox("Select the position you are applying for", ["Data Analyst", "Software Engineer", "Marketing"])
+resume = st.file_uploader("Upload your resume", type=["pdf"])
+auto_play = st.checkbox("Let AI interviewer speak! (Please don't switch during the interview)")
+
+#st.toast("4097 tokens is roughly equivalent to around 800 to 1000 words or 3 minutes of speech. Please keep your answer within this limit.")
+
+@dataclass
+class Message:
+    """Class for keeping track of interview history."""
+    origin: Literal["human", "ai"]
+    message: str
+
+def save_vector(resume):
+    """embeddings"""
+    nltk.download('punkt')
+    pdf_reader = PdfReader(resume)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    # Split the document into chunks
+    text_splitter = NLTKTextSplitter()
+    texts = text_splitter.split_text(text)
+
+    embeddings = OpenAIEmbeddings()
+    docsearch = FAISS.from_texts(texts, embeddings)
+    return docsearch
+
+def initialize_session_state_resume():
+    # convert resume to embeddings
+    if 'docsearch' not in st.session_state:
+        st.session_state.docserch = save_vector(resume)
+    # retriever for resume screen
+    if 'retriever' not in st.session_state:
+        st.session_state.retriever = st.session_state.docserch.as_retriever(search_type="similarity")
+    # prompt for retrieving information
+    if 'chain_type_kwargs' not in st.session_state:
+        st.session_state.chain_type_kwargs = prompt_sector(position, templates)
+    # interview history
+    if "resume_history" not in st.session_state:
+        st.session_state.resume_history = []
+        st.session_state.resume_history.append(Message(origin="ai", message="Hello, I am your interivewer today. I will ask you some questions regarding your resume and your experience. Please start by saying hello or introducing yourself. Note: The maximum length of your answer is 4097 tokens!"))
+    # token count
+    if "token_count" not in st.session_state:
+        st.session_state.token_count = 0
+    # memory buffer for resume screen
+    if "resume_memory" not in st.session_state:
+        st.session_state.resume_memory = ConversationBufferMemory(human_prefix = "Candidate: ", ai_prefix = "Interviewer")
+    # guideline for resume screen
+    if "resume_guideline" not in st.session_state:
+        llm = ChatOpenAI(
+        model_name = "gpt-3.5-turbo",
+        temperature = 0.5,)
+
+        st.session_state.resume_guideline = RetrievalQA.from_chain_type(
+            llm=llm,
+            chain_type_kwargs=st.session_state.chain_type_kwargs, chain_type='stuff',
+            retriever=st.session_state.retriever, memory = st.session_state.resume_memory).run("Create an interview guideline and prepare only two questions for each topic. Make sure the questions tests the knowledge")
+    # llm chain for resume screen
+    if "resume_screen" not in st.session_state:
+        llm = ChatOpenAI(
+            model_name="gpt-3.5-turbo",
+            temperature=0.7, )
+
+        PROMPT = PromptTemplate(
+            input_variables=["history", "input"],
+            template= """I want you to act as an interviewer strictly following the guideline in the current conversation.
+            
+            Ask me questions and wait for my answers like a human. Do not write explanations.
+            Candidate has no assess to the guideline.
+            Only ask one question at a time. 
+            Do ask follow-up questions if you think it's necessary.
+            Do not ask the same question.
+            Do not repeat the question.
+            Candidate has no assess to the guideline.
+            You name is GPTInterviewer.
+            I want you to only reply as an interviewer.
+            Do not write all the conversation at once.
+            Candiate has no assess to the guideline.
+            
+            Current Conversation:
+            {history}
+            
+            Candidate: {input}
+            AI: """)
+        st.session_state.resume_screen =  ConversationChain(prompt=PROMPT, llm = llm, memory = st.session_state.resume_memory)
+    # llm chain for generating feedback
+    if "resume_feedback" not in st.session_state:
+        llm = ChatOpenAI(
+            model_name="gpt-3.5-turbo",
+            temperature=0.5,)
+        st.session_state.resume_feedback = ConversationChain(
+            prompt=PromptTemplate(input_variables=["history","input"], template=templates.feedback_template),
+            llm=llm,
+            memory=st.session_state.resume_memory,
+        )
+
+def answer_call_back():
+    with get_openai_callback() as cb:
+        human_answer = st.session_state.answer
+        if voice:
+            save_wav_file("temp/audio.wav", human_answer)
+            try:
+                input = transcribe("temp/audio.wav")
+                # save human_answer to history
+            except:
+                st.session_state.resume_history.append(Message("ai", "Sorry, I didn't get that."))
+                return "Please try again."
+        else:
+            input = human_answer
+        st.session_state.resume_history.append(
+            Message("human", input)
+        )
+        # OpenAI answer and save to history
+        llm_answer = st.session_state.resume_screen.run(input)
+        # speech synthesis and speak out
+        audio_file_path = synthesize_speech(llm_answer)
+        # create audio widget with autoplay
+        audio_widget = Audio(audio_file_path, autoplay=True)
+        # save audio data to history
+        st.session_state.resume_history.append(
+            Message("ai", llm_answer)
+        )
+        st.session_state.token_count += cb.total_tokens
+        return audio_widget
+
+if position and resume:
+    # intialize session state
+    initialize_session_state_resume()
+    credit_card_placeholder = st.empty()
+    col1, col2 = st.columns(2)
+    with col1:
+        feedback = st.button("Get Interview Feedback")
+    with col2:
+        guideline = st.button("Show me interview guideline!")
+    chat_placeholder = st.container()
+    answer_placeholder = st.container()
+    audio = None
+    # if submit email adress, get interview feedback imediately
+    if guideline:
+        st.markdown(st.session_state.resume_guideline)
+    if feedback:
+        evaluation = st.session_state.resume_feedback.run("please give evalution regarding the interview")
+        st.markdown(evaluation)
+        st.download_button(label="Download Interview Feedback", data=evaluation, file_name="interview_feedback.txt")
+        st.stop()
+    else:
+        with answer_placeholder:
+            voice: bool = st.checkbox("I would like to speak with AI Interviewer!")
+            if voice:
+                answer = audio_recorder(pause_threshold=2, sample_rate=44100)
+                #st.warning("An UnboundLocalError will occur if the microphone fails to record.")
+            else:
+                answer = st.chat_input("Your answer")
+            if answer:
+                st.session_state['answer'] = answer
+                audio = answer_call_back()
+
+        with chat_placeholder:
+            for answer in st.session_state.resume_history:
+                if answer.origin == 'ai':
+                    if auto_play and audio:
+                        with st.chat_message("assistant"):
+                            st.write(answer.message)
+                            st.write(audio)
+                    else:
+                        with st.chat_message("assistant"):
+                            st.write(answer.message)
+                else:
+                    with st.chat_message("user"):
+                        st.write(answer.message)
+
+        credit_card_placeholder.caption(f"""
+                        Progress: {int(len(st.session_state.resume_history) / 30 * 100)}% completed.""")
+