update all

Chatbot.py

@@ -1,29 +1,18 @@
-from openai import OpenAI
-import streamlit as st
-
-with st.sidebar:
-    openai_api_key = st.text_input("OpenAI API Key", key="chatbot_api_key", type="password")
-    "[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
-    "[View the source code](https://github.com/streamlit/llm-examples/blob/main/Chatbot.py)"
-    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
+# Chatbot.py
 
-st.title("💬 Chatbot")
-st.caption("🚀 A streamlit chatbot powered by OpenAI LLM")
-if "messages" not in st.session_state:
-    st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]
+import streamlit as st
 
-for msg in st.session_state.messages:
-    st.chat_message(msg["role"]).write(msg["content"])
+st.set_page_config(
+    page_title="Choose a chatbot💬",
+    page_icon="✅",
+    layout="centered",
+    initial_sidebar_state="collapsed"  # 'collapsed'로 설정하면 사이드바 접힌 상태로 시작
+)
 
-if prompt := st.chat_input():
-    if not openai_api_key:
-        st.info("Please add your OpenAI API key to continue.")
-        st.stop()
+st.markdown("실험에 참여해주셔서 감사합니다!📝")
+st.caption("챗봇🤖을 골라주세요.")
 
-    client = OpenAI(api_key=openai_api_key)
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    st.chat_message("user").write(prompt)
-    response = client.chat.completions.create(model="gpt-3.5-turbo", messages=st.session_state.messages)
-    msg = response.choices[0].message.content
-    st.session_state.messages.append({"role": "assistant", "content": msg})
-    st.chat_message("assistant").write(msg)
+if st.button("Chatbot-S"):
+    st.switch_page("pages/1_Chatbot-S.py")
+if st.button("Chatbot-N"):
+    st.switch_page("pages/2_Chatbot-N.py")

README.md

@@ -1,44 +1,12 @@
-# 🎈 Streamlit + LLM Examples App
-
-[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/streamlit/llm-examples?quickstart=1)
-
-Starter examples for building LLM apps with Streamlit.
+# 🎈 Streamlit + LLM App <Career Counseling Chatbot>
 
 ## Overview of the App
 
-This app showcases a growing collection of LLM minimum working examples.
-
-Current examples include:
-
-- Chatbot
-- File Q&A
-- Chat with Internet search
-- LangChain Quickstart
-- LangChain PromptTemplate
-- Chat with user feedback
-
-## Demo App
-
-[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://llm-examples.streamlit.app/)
-
-### Get an OpenAI API key
+- Chatbot: AI Career Counselor Conversational Assistant 
 
-You can get your own OpenAI API key by following the following instructions:
+## Go to experience the app!
 
-1. Go to https://platform.openai.com/account/api-keys.
-2. Click on the `+ Create new secret key` button.
-3. Next, enter an identifier name (optional) and click on the `Create secret key` button.
-
-### Enter the OpenAI API key in Streamlit Community Cloud
-
-To set the OpenAI API key as an environment variable in Streamlit apps, do the following:
-
-1. At the lower right corner, click on `< Manage app` then click on the vertical "..." followed by clicking on `Settings`.
-2. This brings the **App settings**, next click on the `Secrets` tab and paste the API key into the text box as follows:
-
-```sh
-OPENAI_API_KEY='xxxxxxxxxx'
-```
+[Career Counseling Chatbot App](https://llm-based-career-counseling-chatbot.streamlit.app/)
 
 ## Run it locally
 

__init__.py

@@ -0,0 +1 @@
+import streamlit as st

pages/1_Chatbot-S.py

@@ -0,0 +1,221 @@
+import streamlit as st
+import os, json, chardet, openai
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper
+from langchain_core.tools import tool
+from langchain.memory import ConversationSummaryBufferMemory
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_community.vectorstores import Chroma
+
+# ----------------------------------------------------------------------------------------------------
+# streamlit
+# ----------------------------------------------------------------------------------------------------
+
+st.set_page_config(
+    page_title="Career Counseling Chatbot-S💬",
+    page_icon="💬",
+    layout="wide",
+    initial_sidebar_state="collapsed"  # 'collapsed'로 설정하면 사이드바 접힌 상태로 시작
+)
+
+st.title("Career Counseling Chatbot-S💬")
+st.markdown('진로 결정 어려움을 해결하여 진로 결정을 잘할 수 있도록 도와주는 AI 진로 상담사')
+st.caption("🚀 AI Career Counselor Conversational Assistant produced by Hyerim")
+
+
+OPENAI_API_KEY = st.secrets['OPENAI_API_KEY']
+OPENAI_ORGANIZATION = st.secrets['OPENAI_ORGANIZATION']
+
+# 임베딩 함수 설정
+embedding = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+
+#----------------------------------------------------------------------------------------------------
+# function calling
+# ----------------------------------------------------------------------------------------------------
+# 현재 작업 디렉토리 가져오기
+current_directory = os.getcwd()
+
+# 안전한 경로 설정 (배포 환경에서도 유지됨)
+persist_dir = os.path.join(current_directory, "career_saramin")
+
+@tool
+def SearchCareerInfo(query):
+    """Get the current carrer_info in a given url"""
+    career_info = None
+    tavily_search = TavilySearchAPIWrapper(tavily_api_key=st.secrets['TAVILY_API'])
+    # 쿼리 작성
+    url = "https://www.work.go.kr/consltJobCarpa/srch/getExpTheme.do?jobClcd=D&pageIndex=1&pageUnit=10"
+
+    # TavilySearchAPIWrapper를 이용하여 검색 결과 가져오기
+    search_results = TavilySearchResults(max_results=5, tavily_api_key=st.secrets['TAVILY_API']).invoke(query)
+    career_info = {
+        "name": career_info,
+        "query": query,
+        "career_info": search_results,
+    }
+    return json.dumps(career_info)
+
+@tool
+def SearchSeniorInfo(query):
+    """Get the current Senior_info in RAG"""
+    senior_info = None
+    embedding = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+    # load from disk (save 이후)
+
+    try:
+        vector = Chroma(persist_directory=persist_dir, embedding_function=embedding, collection_name="career_saramin")
+        print("ChromaDB 문서 개수:", vector._collection.count())
+    except Exception as e:
+        print("Error Loading ChromaDB:", e)
+    # ✅ RAG 기반 검색 수행
+    search_results = vector.similarity_search(query, k=3)  # 🔥 상위 1개 문서 검색
+    # page_content만 리스트로 추출
+    page_contents = [doc.page_content for doc in search_results]
+
+    senior_info = {
+        "name": "senior_info",
+        "query": query,
+        "careersenior_info": page_contents,
+    }
+    return senior_info
+
+tools = [
+    {
+        "name": "SearchCareerInfo",
+        "description": "A tool that search the information needed to generate job information according to the prompt based on the given URL.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "The search query to look for specific Career information."
+                },
+                # "career_info": {
+                #     "type": "string",
+                #     "description": "Career information Retrieved by query."
+                # }                
+            },
+            # "required": ["query", "career_info"]
+            "required": ["query"]
+        },
+    },
+    {
+        "name": "SearchSeniorInfo",
+        "description": "A retrieval-augmented tool that searches senior career insights based on past interviews and expert testimonials. This tool utilizes ChromaDB and OpenAI Embeddings to find the most relevant career growth strategies, job challenges, and expert advice.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "The search query to look for specific Senior based Career information."
+                },           
+            },
+            "required": ["query"]
+        },
+    }
+]
+#----------------------------------------------------------------------------------------------------
+# Prompt Template
+# ----------------------------------------------------------------------------------------------------
+
+
+# ----------------------------------------------------------------------------------------------------
+# RAG 
+#-----------------------------------------------------------------------------------------------------
+#-------------------------------------------------------------------------------------------------
+# Session State
+#----------------------------------------------------------------------------------------------------
+
+
+llm = ChatOpenAI(model_name="gpt-4o", 
+                 temperature=0.5, 
+                 openai_api_key=OPENAI_API_KEY, 
+                 organization=OPENAI_ORGANIZATION)
+
+#llm.bind_tools(tools=[SearchCareerInfo, SearchSeniorInfo])
+# tool_choice=[SearchCareerInfo]
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = [
+        {"role": "system", "content": st.secrets["system_prompt_S"]},
+        {"role": "assistant", "content": "안녕하세요! 저는 오늘 당신과 함께 진로 고민에 대해 이야기 나눠볼 AI 진로 상담사입니다.😊 제가 어떻게 불러주면 좋을까요?"}
+    ]
+if "memory" not in st.session_state:
+     st.session_state.memory = ConversationSummaryBufferMemory(
+          llm=llm,
+          max_token_limit=1000,  # 요약의 기준이 되는 토큰 길이를 설정합니다.
+          return_messages=True,
+          )
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:        
+    if message["role"]=='system':
+        continue
+    st.chat_message(message["role"]).write(message["content"]) 
+    print(message) 
+
+if user_input := st.chat_input(): 
+    #Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": user_input})
+    st.chat_message("user").write(user_input)
+
+    with st.spinner('Please wait...'):
+        response = openai.chat.completions.create(
+            model="gpt-4o",
+            messages=st.session_state.messages,
+            functions=tools,
+            function_call="auto"
+        )
+        response_message = response.choices[0].message
+        if response_message.function_call:
+            function_name = response_message.function_call.name
+            function_args = json.loads(response_message.function_call.arguments)
+            if function_name == "SearchCareerInfo":
+                # 함수 실행
+                function_response = SearchCareerInfo(function_name)
+                parsed_data = json.loads(function_response)
+                for info in parsed_data['career_info']:
+                    detected = chardet.detect(info['content'].encode())
+                    decoded_content = info['content'].encode(detected['encoding']).decode('utf-8', errors='ignore') #한국어로 디코딩
+                # 함수 응답을 메시지 이력에 추가
+                st.session_state.messages.append({
+                    "role": "function",
+                    "name": function_name,
+                    "content": decoded_content
+                })
+                assistant_reply= openai.chat.completions.create(
+                    model="gpt-4o",
+                    messages=st.session_state.messages
+                    )
+                assistant_replys = assistant_reply.choices[0].message.content
+            elif function_name == "SearchSeniorInfo":
+                # 함수 실행
+                #function_response = SearchSeniorInfo(function_name)
+                function_response = SearchSeniorInfo(function_args["query"])
+                # 함수 응답을 메시지 이력에 추가
+                st.session_state.messages.append({
+                    "role": "function",
+                    "name": function_name,
+                    "content": function_response["careersenior_info"]
+                })
+                assistant_reply= openai.chat.completions.create(
+                    model="gpt-4o",
+                    messages=st.session_state.messages
+                )
+                assistant_replys = assistant_reply.choices[0].message.content
+        else:        
+            assistant_replys = response.choices[0].message.content
+        # Add assistant response to chat history
+        st.chat_message("assistant").write(assistant_replys)  
+    st.session_state.messages.append({"role": "assistant", "content": assistant_replys})    
+    st.session_state.memory.save_context(inputs={"user": user_input}, outputs={"assistant": assistant_replys})
+
+
+# 대화 로그 저장
+def save_conversation_to_file(conversation):
+    with open("career_chat_log.csv", mode='a', newline='', encoding='utf-8') as file:  
+        for message in conversation:
+                if message["role"]=='system':
+                    continue
+                file.write(f"{message['role']}: {message['content']}\n")