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| import streamlit as st | |
| import html | |
| from openai import OpenAI | |
| import os | |
| # Load environment variables | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| api_key = os.getenv("OPENAI_API_KEY") | |
| # Initialize the OpenAI client | |
| client = OpenAI(api_key=api_key) | |
| def openai_chat(prompt, chat_log): | |
| context_messages = [ | |
| {"role": "system", "content": """You are a gifted C++ professor. You explain complex C++ | |
| concepts clearly using words that a | |
| college student would understand, and generate typical exam questions for a C++ course. After a few questions, | |
| three or four, check in with the student to ask if you are helpful and if the student is prepared for the exam | |
| or stuck on a particular topic, or just needs a cram session before the exam. Be supportive and motivational. | |
| Suggest getting a good night's sleep and eating properly before the exam when saying goodbye. After answering | |
| a question from the student, suggest three or four C++ final exam questions and related topics when asked anything.""" | |
| }, | |
| {"role": "user", "content": "Explain recursion in C++ programming."} | |
| ] + chat_log + [{"role": "user", "content": prompt}] | |
| try: | |
| completion = client.chat.completions.create( | |
| model="gpt-3.5-turbo", | |
| messages=context_messages, | |
| max_tokens=500 | |
| ) | |
| response_text = html.unescape(completion.choices[0].message.content) | |
| chat_log.append({"role": "assistant", "content": response_text}) | |
| return response_text, chat_log | |
| except Exception as e: | |
| return str(e), chat_log | |
| def format_response(answer): | |
| # Only apply Markdown to code responses | |
| if 'int main()' in answer or '#include' in answer or 'std::' in answer: | |
| code_block = "```cpp\n" + answer + "\n```" | |
| return code_block | |
| return answer | |
| def main(): | |
| st.title("Professor CplusPlus") | |
| st.write("Ask any question about C++, and I'll explain!") | |
| if 'chat_log' not in st.session_state: | |
| st.session_state.chat_log = [] | |
| if 'history' not in st.session_state: | |
| st.session_state.history = "" | |
| user_input = st.text_input("Type your question here:", key="user_input") | |
| if st.button("Ask") and user_input: | |
| answer, st.session_state.chat_log = openai_chat(user_input, st.session_state.chat_log) | |
| formatted_answer = format_response(answer) | |
| new_entry = f"Q: {user_input}\n\nA: {formatted_answer}\n\n" | |
| st.session_state.history = new_entry + st.session_state.history | |
| st.rerun() # Using the updated rerun method | |
| st.write("Chat History:") | |
| st.markdown(st.session_state.history, unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() | |