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| from langchain.llms import OpenAI | |
| from langchain.chains.qa_with_sources import load_qa_with_sources_chain | |
| from langchain.docstore.document import Document | |
| import requests | |
| import pathlib | |
| import subprocess | |
| import tempfile | |
| import os | |
| import gradio as gr | |
| import pickle | |
| # using a vector space for our search | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.vectorstores.faiss import FAISS | |
| from langchain.text_splitter import CharacterTextSplitter | |
| #To get markdowns from github fo Gradio (/or your) repo | |
| def get_github_docs(repo_owner, repo_name): | |
| with tempfile.TemporaryDirectory() as d: | |
| subprocess.check_call( | |
| f"git clone https://github.com/{repo_owner}/{repo_name}.git .", | |
| cwd=d, | |
| shell=True, | |
| ) | |
| git_sha = ( | |
| subprocess.check_output("git rev-parse HEAD", shell=True, cwd=d) | |
| .decode("utf-8") | |
| .strip() | |
| ) | |
| repo_path = pathlib.Path(d) | |
| markdown_files = list(repo_path.rglob("*.md")) + list( | |
| repo_path.rglob("*.mdx") | |
| ) | |
| for markdown_file in markdown_files: | |
| try: | |
| with open(markdown_file, "r") as f: | |
| relative_path = markdown_file.relative_to(repo_path) | |
| github_url = f"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}" | |
| yield Document(page_content=f.read(), metadata={"source": github_url}) | |
| except FileNotFoundError: | |
| print(f"Could not open file: {markdown_file}") | |
| #sources = get_github_docs("gradio-app", "gradio") | |
| #source_chunks = [] | |
| #splitter = CharacterTextSplitter(separator=" ", chunk_size=1024, chunk_overlap=0) | |
| #for source in sources: | |
| # for chunk in splitter.split_text(source.page_content): | |
| # source_chunks.append(Document(page_content=chunk, metadata=source.metadata)) | |
| #search_index = FAISS.from_documents(source_chunks, OpenAIEmbeddings()) #(source_chunks, OpenAIEmbeddings()) # <------ | |
| #chain = load_qa_with_sources_chain(OpenAI(temperature=0)) ## <<--------- | |
| #loading FAISS search index from disk | |
| with open("search_index.pickle", "rb") as f: | |
| search_index = pickle.load(f) | |
| def print_answer(question, openai): #openai_embeddings | |
| #search_index = get_search_index() | |
| chain = load_qa_with_sources_chain(openai) #(OpenAI(temperature=0)) | |
| response = ( | |
| chain( | |
| { | |
| "input_documents": search_index.similarity_search(question, k=4), | |
| "question": question, | |
| }, | |
| return_only_outputs=True, | |
| )["output_text"] | |
| ) | |
| #print(response) | |
| if len(response.split('\n')[-1].split())>2: | |
| response = response.split('\n')[0] + ', '.join([' <a href="' + response.split('\n')[-1].split()[i] + '" target="_blank"><u>Click Link' + str(i) + '</u></a>' for i in range(1,len(response.split('\n')[-1].split()))]) | |
| else: | |
| response = response.split('\n')[0] + ' <a href="' + response.split('\n')[-1].split()[-1] + '" target="_blank"><u>Click Link</u></a>' | |
| return response | |
| def chat(message, history, openai_api_key): | |
| #openai_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) | |
| openai = OpenAI(temperature=0, openai_api_key=openai_api_key ) | |
| #os.environ["OPENAI_API_KEY"] = openai_api_key | |
| history = history or [] | |
| message = message.lower() | |
| response = print_answer(message, openai) #openai_embeddings | |
| history.append((message, response)) | |
| return history, history | |
| #chatbot = gr.Chatbot().style(color_map=("green", "orange")) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("""<h1><centre>LangChain - powered - Gradio-Helper-Bot </h1></centre> | |
| """) | |
| with gr.Row(): | |
| question = gr.Textbox(label = 'Type in your questions about Gradio here', placeholder = 'What is the role of "every" argument in a component') | |
| openai_api_key = gr.Textbox(type='password') | |
| state = gr.State() | |
| chatbot = gr.Chatbot() | |
| question.submit(chat, [question, state, openai_api_key], [chatbot, state]) | |
| if __name__ == "__main__": | |
| demo.launch() | |