Update app.py

app.py

@@ -27,43 +27,26 @@ def load_vector_db(zip_file_path, extract_path):
     st.success("Vector store loaded")
     return vectordb
 
-# # Function to augment prompt
-# def augment_prompt(query, vectordb):
-#     results = vectordb.similarity_search(query, k=10)
-#     source_knowledge = "\n".join([x.page_content for x in results])
-#     augmented_prompt = f"""
-#     You are an AI assistant. Use the context provided below to answer the question as comprehensively as possible. 
-#     If the answer is not contained within the context, respond politely that you cannot provide that information.
-
-#     Context:
-#     {source_knowledge}
-
-#     Question: {query}
-#     """
-#     return augmented_prompt
-
 # Function to augment prompt
-def augment_prompt(query, vectordb, search_results):
-    results = vectordb.similarity_search(query, k=5)
+def augment_prompt(query, vectordb):
+    results = vectordb.similarity_search(query, k=10)
     source_knowledge = "\n".join([x.page_content for x in results])
     augmented_prompt = f"""
     You are an AI assistant. Use the context provided below to answer the question as comprehensively as possible. 
-    If the answer is not contained within the context, respond with "I don't know".
+    If the answer is not contained within the context, respond politely that you cannot provide that information.
 
     Context:
     {source_knowledge}
 
-    Additional Web Search Results:
-    {search_results}
-
     Question: {query}
     """
     return augmented_prompt
 
+
 # Function to handle chat with OpenAI
-def chat_with_openai(query, vectordb, openai_api_key, search_results):
-    chat = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=openai_api_key, timeout=30)  # Increased timeout
-    augmented_query = augment_prompt(query, vectordb, search_results)
+def chat_with_openai(query, vectordb, openai_api_key):
+    chat = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=openai_api_key)
+    augmented_query = augment_prompt(query, vectordb)
     prompt = HumanMessage(content=augmented_query)
     messages = [
         SystemMessage(content="You are a helpful assistant."),
@@ -72,37 +55,6 @@ def chat_with_openai(query, vectordb, openai_api_key, search_results):
     res = chat(messages)
     return res.content
 
-# Function to perform web search
-def perform_web_search(query):
-    headers = {
-        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3"}
-    search_results = ""
-
-    # Glassdoor search
-    glassdoor_url = f"https://www.glassdoor.com/Search/results.htm?keyword={query}"
-    response = requests.get(glassdoor_url, headers=headers)
-    if response.status_code == 200:
-        soup = BeautifulSoup(response.text, 'html.parser')
-        glassdoor_results = soup.find_all('div', {'class': 'jobContainer'})
-        for result in glassdoor_results[:5]:  # limiting to first 3 results
-            title = result.find('a', {'class': 'jobInfoItem jobTitle'}).text.strip() if result.find('a', {'class': 'jobInfoItem jobTitle'}) else 'N/A'
-            company = result.find('div', {'class': 'jobInfoItem jobEmpolyerName'}).text.strip() if result.find('div', {'class': 'jobInfoItem jobEmpolyerName'}) else 'N/A'
-            location = result.find('span', {'class': 'subtle loc'}).text.strip() if result.find('span', {'class': 'subtle loc'}) else 'N/A'
-            search_results += f"Glassdoor Result: {title} at {company}, {location}\n"
-
-    # Indeed search
-    indeed_url = f"https://www.indeed.com/jobs?q={query}&limit=10"
-    response = requests.get(indeed_url, headers=headers)
-    if response.status_code == 200:
-        soup = BeautifulSoup(response.text, 'html.parser')
-        indeed_results = soup.find_all('div', {'class': 'jobsearch-SerpJobCard'})
-        for result in indeed_results[:5]:  # limiting to first 3 results
-            title = result.find('h2', {'class': 'title'}).text.strip() if result.find('h2', {'class': 'title'}) else 'N/A'
-            company = result.find('span', {'class': 'company'}).text.strip() if result.find('span', {'class': 'company'}) else 'N/A'
-            location = result.find('span', {'class': 'location'}).text.strip() if result.find('span', {'class': 'location'}) else 'N/A'
-            search_results += f"Indeed Result: {title} at {company}, {location}\n"
-    
-    return search_results
 
 # Streamlit UI
 st.title("Data Roles Company Finder Chatbot")
@@ -122,69 +74,19 @@ for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
+
 # User input
 if prompt := st.chat_input("Enter your query"):
     st.session_state.messages.append({"role": "user", "content": prompt})
     with st.chat_message("user"):
         st.markdown(prompt)
 
-    # Perform web search
-    search_results = perform_web_search(prompt)
-
-    # Chat with OpenAI
-    openai_api_key = st.secrets["OPENAI_API_KEY"]
-    response = chat_with_openai(prompt, vectordb, openai_api_key, search_results)
-    
-    # Display assistant response
     with st.chat_message("assistant"):
+        openai_api_key = st.secrets["OPENAI_API_KEY"]
+        response = chat_with_openai(prompt, vectordb, openai_api_key)
         st.markdown(response)
         
     st.session_state.messages.append({"role": "assistant", "content": response})
-    
-# # Function to handle chat with OpenAI
-# def chat_with_openai(query, vectordb, openai_api_key):
-#     chat = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key=openai_api_key)
-#     augmented_query = augment_prompt(query, vectordb)
-#     prompt = HumanMessage(content=augmented_query)
-#     messages = [
-#         SystemMessage(content="You are a helpful assistant."),
-#         prompt
-#     ]
-#     res = chat(messages)
-#     return res.content
-
-
-# # Streamlit UI
-# st.title("Data Roles Company Finder Chatbot")
-# st.write("This app helps users find companies hiring for data roles, providing information such as job title, salary estimate, job description, company rating, and more.")
-
-# # Load vector database
-# zip_file_path = "chroma_db_compressed_.zip"
-# extract_path = "./chroma_db_extracted"
-# vectordb = load_vector_db(zip_file_path, extract_path)
-
-# # Initialize session state for chat history
-# if "messages" not in st.session_state:
-#     st.session_state.messages = []
-
-# # Display chat history
-# for message in st.session_state.messages:
-#     with st.chat_message(message["role"]):
-#         st.markdown(message["content"])
-
-
-# # User input
-# if prompt := st.chat_input("Enter your query"):
-#     st.session_state.messages.append({"role": "user", "content": prompt})
-#     with st.chat_message("user"):
-#         st.markdown(prompt)
-
-#     with st.chat_message("assistant"):
-#         openai_api_key = st.secrets["OPENAI_API_KEY"]
-#         response = chat_with_openai(prompt, vectordb, openai_api_key)
-#         st.markdown(response)
-        
-#     st.session_state.messages.append({"role": "assistant", "content": response})
         
 # # Query input
 # query = st.text_input("Enter your query", "")