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| import gradio as gr | |
| import openai | |
| import requests | |
| import csv | |
| import os | |
| import langchain | |
| import chromadb | |
| import glob | |
| #import pickle | |
| from PyPDF2 import PdfReader | |
| from PyPDF2 import PdfWriter | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.text_splitter import CharacterTextSplitter | |
| #from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS | |
| from langchain.chains.question_answering import load_qa_chain | |
| from langchain.llms import OpenAI | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| #from langchain.vectorstores import Chroma | |
| #from langchain.text_splitter import TokenTextSplitter | |
| #from langchain.llms import OpenAI | |
| from langchain import OpenAI | |
| from langchain.chat_models import ChatOpenAI | |
| #from langchain.chains import ChatVectorDBChain | |
| #from langchain.chains import RetrievalQA | |
| from langchain.document_loaders import PyPDFLoader | |
| from langchain.chains.question_answering import load_qa_chain | |
| # Use Chroma in Colab to create vector embeddings, I then saved them to HuggingFace so now I have to set it use them here. | |
| #from chromadb.config import Settings | |
| #client = chromadb.Client(Settings( | |
| ## chroma_db_impl="duckdb+parquet", | |
| # persist_directory="./embeddings" # Optional, defaults to .chromadb/ in the current directory | |
| #)) | |
| openai.api_key = os.environ['openai_key'] | |
| os.environ["OPENAI_API_KEY"] = os.environ['openai_key'] | |
| def get_empty_state(): | |
| return {"total_tokens": 0, "messages": []} | |
| #Initial prompt template, others added below from TXT file | |
| prompt_templates = {"All Needs Experts": "Respond as if you are combiation of all needs assessment experts."} | |
| actor_description = {} | |
| def download_prompt_templates(): | |
| url = "https://huggingface.co/spaces/ryanrwatkins/needs/raw/main/gurus.txt" | |
| try: | |
| response = requests.get(url) | |
| reader = csv.reader(response.text.splitlines()) | |
| next(reader) # skip the header row | |
| for row in reader: | |
| if len(row) >= 2: | |
| act = row[0].strip('"') | |
| prompt = row[1].strip('"') | |
| description = row[2].strip('"') | |
| prompt_templates[act] = prompt | |
| actor_description[act] = description | |
| except requests.exceptions.RequestException as e: | |
| print(f"An error occurred while downloading prompt templates: {e}") | |
| return | |
| choices = list(prompt_templates.keys()) | |
| choices = choices[:1] + sorted(choices[1:]) | |
| return gr.update(value=choices[0], choices=choices) | |
| def on_prompt_template_change(prompt_template): | |
| if not isinstance(prompt_template, str): return | |
| return prompt_templates[prompt_template] | |
| def on_prompt_template_change_description(prompt_template): | |
| if not isinstance(prompt_template, str): return | |
| return actor_description[prompt_template] | |
| def submit_message(prompt, prompt_template, temperature, max_tokens, context_length, state): | |
| # load in all the files | |
| #path = './files' | |
| #pdf_files = glob.glob(os.path.join(path, "*.pdf")) | |
| #pdf_files = glob.glob(os.path.join(path, "*.pdf")) | |
| #for file in pdf_files: | |
| # loader = PyPDFLoader(file) | |
| # pages = loader.load_and_split() | |
| # text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=0) | |
| # split_pages = text_splitter.split_documents(pages) | |
| #persist_directory = "./embeddings" | |
| #embeddings = OpenAIEmbeddings() | |
| #vectordb = Chroma.from_documents(split_pages, embeddings, persist_directory=persist_directory) | |
| #vectordb.persist() | |
| path = './files' | |
| pdf_files = glob.glob(os.path.join(path, "*.pdf")) | |
| merger = PdfWriter() | |
| # add all file in the list to the merger object | |
| for pdf in pdf_files: | |
| merger.append(pdf) | |
| merger.write("merged-pdf.pdf") | |
| merger.close() | |
| reader = PdfReader("merged-pdf.pdf") | |
| raw_text = '' | |
| for i, page in enumerate(reader.pages): | |
| text = page.extract_text() | |
| if text: | |
| raw_text += text | |
| text_splitter = CharacterTextSplitter( | |
| separator = "\n", | |
| chunk_size = 1000, | |
| chunk_overlap = 200, | |
| length_function = len, | |
| ) | |
| texts = text_splitter.split_text(raw_text) | |
| len(texts) | |
| embeddings = OpenAIEmbeddings() | |
| history = state['messages'] | |
| if not prompt: | |
| return gr.update(value=''), [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)], f"Total tokens used: {state['total_tokens']}", state | |
| prompt_template = prompt_templates[prompt_template] | |
| system_prompt = [] | |
| if prompt_template: | |
| system_prompt = [{ "role": "system", "content": prompt_template }] | |
| prompt_msg = { "role": "user", "content": prompt } | |
| try: | |
| #completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens) | |
| # completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=system_prompt + history[-context_length*2:] + [prompt_msg], temperature=temperature, max_tokens=max_tokens) | |
| #completion_chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff" ) | |
| #completion = RetrievalQA(combine_documents_chain=completion_chain, retriever=vectordb.as_retriever()) | |
| #query = str(system_prompt + history[-context_length*2:] + [prompt_msg]) | |
| #completion = completion.run(query) | |
| # from https://blog.devgenius.io/chat-with-document-s-using-openai-chatgpt-api-and-text-embedding-6a0ce3dc8bc8 | |
| #completion_chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff" ) | |
| #completion = RetrievalQA(combine_documents_chain=completion_chain, retriever=vectordb.as_retriever(), return_source_documents=False) | |
| #completion = RetrievalQA.from_chain_type(llm=ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff", retriever=vectordb.as_retriever(), return_source_documents=True) | |
| #query = str(system_prompt + history[-context_length*2:] + [prompt_msg]) | |
| #completion = completion({"query": query}) | |
| #completion = completion.run(query) | |
| # completion = completion({"question": query, "chat_history": history[-context_length*2:]}) | |
| #with open("foo.pkl", 'rb') as f: | |
| # new_docsearch = pickle.load(f) | |
| docsearch = FAISS.from_texts(texts, embeddings) | |
| #query = str(system_prompt + history[-context_length*2:] + [prompt_msg]) | |
| query = str(system_prompt + history + [prompt_msg]) | |
| docs = docsearch.similarity_search(query) | |
| #print(docs[0].page_content) | |
| chain = load_qa_chain(ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model_name="gpt-3.5-turbo"), chain_type="stuff") | |
| completion = chain.run(input_documents=docs, question=query) | |
| completion = { "content": completion } | |
| # VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=docsearch, return_source_documents=True) | |
| # https://colab.research.google.com/drive/1dzdNDZyofRB0f2KIB4gHXmIza7ehMX30?usp=sharing#scrollTo=b-ejDn_JfpWW | |
| get_empty_state() | |
| state.append(completion.copy()) | |
| #history.append(prompt_msg.copy()) | |
| #history.append(completion.copy()) | |
| #history.append(completion.choices[0].message.to_dict()) | |
| #history.append(completion["result"].choices[0].message.to_dict()) | |
| state['total_tokens'] += completion['usage']['total_tokens'] | |
| except Exception as e: | |
| history.append(prompt_msg.copy()) | |
| error = { | |
| "role": "system", | |
| "content": f"Error: {e}" | |
| } | |
| history.append(error.copy()) | |
| total_tokens_used_msg = f"Total tokens used: {state['total_tokens']}" | |
| chat_messages = [(prompt_msg['content'], completion['content'])] | |
| #chat_messages = [(history[i]['content'], history[i+1]['content']) for i in range(0, len(history)-1, 2)] | |
| #chat_messages = [(history[-2]['content'], history[-1]['content'])] | |
| return '', chat_messages, total_tokens_used_msg, state | |
| def clear_conversation(): | |
| return gr.update(value=None, visible=True), None, "", get_empty_state() | |
| css = """ | |
| #col-container {max-width: 80%; margin-left: auto; margin-right: auto;} | |
| #chatbox {min-height: 400px;} | |
| #header {text-align: center;} | |
| #prompt_template_preview {padding: 1em; border-width: 1px; border-style: solid; border-color: #e0e0e0; border-radius: 4px;} | |
| #total_tokens_str {text-align: right; font-size: 0.8em; color: #666;} | |
| #label {font-size: 0.8em; padding: 0.5em; margin: 0;} | |
| .message { font-size: 1.2em; } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| state = gr.State(get_empty_state()) | |
| with gr.Column(elem_id="col-container"): | |
| #with open("embeddings.pkl", 'rb') as f: | |
| # new_docsearch = pickle.load(f) | |
| #query = str("performance") | |
| #docs = new_docsearch.similarity_search(query) | |
| gr.Markdown("""# Chat with Needs Assessment Experts (Past and Present) | |
| ## Ask questions of experts on needs assessments, get responses from *needs assessment* version of ChatGPT. | |
| Ask questions of all of them, or pick your expert.""" , | |
| elem_id="header") | |
| with gr.Row(): | |
| with gr.Column(): | |
| chatbot = gr.Chatbot(elem_id="chatbox") | |
| input_message = gr.Textbox(show_label=False, placeholder="Enter your needs assessment question and press enter", visible=True).style(container=False) | |
| btn_submit = gr.Button("Submit") | |
| total_tokens_str = gr.Markdown(elem_id="total_tokens_str") | |
| btn_clear_conversation = gr.Button("Start New Conversation") | |
| with gr.Column(): | |
| prompt_template = gr.Dropdown(label="Choose a expert:", choices=list(prompt_templates.keys())) | |
| prompt_template_preview = gr.Markdown(elem_id="prompt_template_preview") | |
| with gr.Accordion("Advanced parameters", open=False): | |
| temperature = gr.Slider(minimum=0, maximum=2.0, value=0.7, step=0.1, label="Flexibility", info="Higher = more creative/chaotic, Lower = just the guru") | |
| max_tokens = gr.Slider(minimum=100, maximum=400, value=200, step=1, label="Max tokens per response") | |
| context_length = gr.Slider(minimum=1, maximum=5, value=2, step=1, label="Context length", info="Number of previous questions you have asked. Be careful with high values, it can blow up the token budget quickly.") | |
| btn_submit.click(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state]) | |
| input_message.submit(submit_message, [ input_message, prompt_template, temperature, max_tokens, context_length, state], [input_message, chatbot, total_tokens_str, state]) | |
| btn_clear_conversation.click(clear_conversation, [], [input_message, chatbot, total_tokens_str, state]) | |
| #prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview]) | |
| prompt_template.change(on_prompt_template_change, inputs=[prompt_template], outputs=[prompt_template_preview]) | |
| demo.load(download_prompt_templates, inputs=None, outputs=[prompt_template], queur=False) | |
| demo.queue(concurrency_count=10) | |
| demo.launch(height='800px') | |