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| import os | |
| import time | |
| import openai | |
| import gradio as gr | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores import Chroma | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.schema import AIMessage, HumanMessage, SystemMessage | |
| # Sets up OpenAI embeddings model | |
| embeddings = OpenAIEmbeddings() | |
| # Loads database from persisted directory | |
| db_directory = "chroma_db" | |
| db = Chroma(persist_directory=db_directory, embedding_function=embeddings) | |
| # Retrieves relevant documents based on a similarity search | |
| retriever = db.as_retriever(search_type='similarity', search_kwargs={"k":3}) | |
| with open('system_prompt.txt', 'r') as file: | |
| ORIG_SYSTEM_MESSAGE_PROMPT = file.read() | |
| openai.api_key = os.getenv("OPENAI_API_KEY") | |
| chat = ChatOpenAI(model_name="gpt-4",temperature=0) | |
| # Here is the langchain | |
| def predict(history, input): | |
| context = retriever.get_relevant_documents(input) | |
| print(context) #For debugging | |
| history_langchain_format = [] | |
| history_langchain_format.append(SystemMessage(content=f"{ORIG_SYSTEM_MESSAGE_PROMPT}")) | |
| for human, ai in history: | |
| history_langchain_format.append(HumanMessage(content=human)) | |
| history_langchain_format.append(AIMessage(content=ai)) | |
| history_langchain_format.append(HumanMessage(content=input)) | |
| history_langchain_format.append(SystemMessage(content=f"Here are some stories the user may like: {context}")) | |
| gpt_response = chat(history_langchain_format) | |
| # Extract pairs of HumanMessage and AIMessage | |
| pairs = [] | |
| for i in range(len(history_langchain_format)): | |
| if isinstance(history_langchain_format[i], HumanMessage) and (i+1 < len(history_langchain_format)) and isinstance(history_langchain_format[i+1], AIMessage): | |
| pairs.append((history_langchain_format[i].content, history_langchain_format[i+1].content)) | |
| # Add new AI response to the pairs for subsequent interactions | |
| pairs.append((input, gpt_response.content)) | |
| return pairs | |
| # Function to handle user message | |
| def user(user_message, chatbot_history): | |
| return "", chatbot_history + [[user_message, ""]] | |
| # Function to handle AI's response | |
| def bot(chatbot_history): | |
| user_message = chatbot_history[-1][0] #This line is because we cleared the user_message previously in the user function above | |
| # Call the predict function to get the AI's response | |
| pairs = predict(chatbot_history, user_message) | |
| _, ai_response = pairs[-1] # Get the latest response | |
| response_in_progress = "" | |
| for character in ai_response: | |
| response_in_progress += character | |
| chatbot_history[-1][1] = response_in_progress | |
| time.sleep(0.05) | |
| yield chatbot_history | |
| # This is a function to do something with the voted information | |
| def vote(data: gr.LikeData): | |
| if data.liked: | |
| print("You upvoted this response: " + data.value) | |
| else: | |
| print("You downvoted this response: " + data.value) | |
| with open("logs.txt", "a") as text_file: | |
| print(f"Disliked content: {data.value}", file=text_file) | |
| # The Gradio App interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("""<h1><center>Technocomplex Bot</center></h1>""") | |
| gr.Markdown("""<h3><center>This is a demo for Our Complex Relationships with Technology course, Duke, 2023</center></h3>""") | |
| chatbot = gr.Chatbot(label="Technocomplex Bot") | |
| textbox = gr.Textbox(label="Start chatting here and click 'Enter' to submit") | |
| clear = gr.Button("Clear") | |
| # Chain user and bot functions with `.then()` | |
| textbox.submit(user, [textbox, chatbot], [textbox, chatbot], queue=False).then( | |
| bot, chatbot, chatbot, | |
| ) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| chatbot.like(vote, None, None) | |
| # Enable queuing | |
| demo.queue() | |
| demo.launch(debug=True, share=True) |