# from typing import Any, Coroutine from uuid import UUID from langchain.schema.agent import AgentAction, AgentFinish import openai import os # from langchain.vectorstores import Chroma from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter from langchain.chat_models import AzureChatOpenAI from langchain.document_loaders import DirectoryLoader from langchain.chains import RetrievalQA # from langchain.vectorstores import Pinecone from langchain.vectorstores.pinecone import Pinecone from langchain.agents import initialize_agent from langchain.agents import AgentType from langchain.agents import Tool # from langchain.agents import load_tools from langchain.tools import BaseTool from langchain.tools import DuckDuckGoSearchRun from langchain.utilities import WikipediaAPIWrapper from langchain.python import PythonREPL from langchain.chains import LLMMathChain from langchain.memory import ConversationBufferMemory from langchain.memory import ConversationBufferWindowMemory from langchain.agents import ZeroShotAgent, AgentExecutor from langchain.agents import OpenAIMultiFunctionsAgent from langchain.prompts import MessagesPlaceholder from langchain.chains.summarize import load_summarize_chain from langchain.schema.messages import ( AIMessage, BaseMessage, FunctionMessage, SystemMessage, ) # from langchain import LLMChain from langchain.chains import LLMChain import azure.cognitiveservices.speech as speechsdk import requests import sys import pinecone from pinecone.core.client.configuration import Configuration as OpenApiConfiguration import gradio as gr import time import glob from typing import Any, Dict, List, Mapping, Optional from multiprocessing import Pool from tqdm import tqdm from pygame import mixer from langchain.document_loaders import ( CSVLoader, EverNoteLoader, PyMuPDFLoader, TextLoader, UnstructuredEmailLoader, UnstructuredEPubLoader, UnstructuredHTMLLoader, UnstructuredMarkdownLoader, UnstructuredODTLoader, UnstructuredPowerPointLoader, UnstructuredWordDocumentLoader, UnstructuredExcelLoader ) from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.text_splitter import TokenTextSplitter from langchain.docstore.document import Document import langchain import asyncio from playwright.async_api import async_playwright from langchain.embeddings.huggingface import HuggingFaceEmbeddings from langchain.llms.base import LLM from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.schema import ( Generation, LLMResult ) import time from datasets import load_dataset from transformers import pipeline import soundfile as sf from scipy.io import wavfile import re from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan import torch from codeinterpreterapi import CodeInterpreterSession import html2text from interpreter.code_interpreter import CodeInterpreter # from interpreter.code_block import CodeBlock import regex from langchain.callbacks.base import BaseCallbackHandler from collections.abc import Generator from queue import Queue, Empty from threading import Thread class QueueCallback(BaseCallbackHandler): """Callback handler for streaming LLM responses to a queue.""" def __init__(self, q): self.q = q def on_llm_new_token(self, token: str, **kwargs: any) -> None: self.q.put(token) def on_agent_finish(self, finish: AgentFinish, *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any) -> Any: self.q.put(super().on_agent_finish(finish, run_id=run_id, parent_run_id=parent_run_id, **kwargs)) def on_agent_action(self, action: AgentAction, *, run_id: UUID, parent_run_id: UUID | None = None, **kwargs: Any) -> Any: self.q.put(super().on_agent_action(action, run_id=run_id, parent_run_id=parent_run_id, **kwargs)) def on_llm_end(self, *args, **kwargs: any) -> None: return self.q.empty() def stream(input_text) -> Generator: # Create a Queue q = Queue() job_done = object() # Create a funciton to call - this will run in a thread def task(): resp = agent.run(input_text, callbacks=[QueueCallback(q)]) q.put(job_done) # Create a thread and start the function t = Thread(target=task) t.start() content = "" # Get each new token from the queue and yield for our generator counter = 0 while True: try: next_token = q.get(True, timeout=60) print("next_token: ", str(next_token)) if next_token == None: next_token = "" # counter = counter + 1 # print("No data, retry number: ", counter) if counter > 3: break if next_token is job_done: break content += str(next_token) yield next_token, content except Empty: continue global CurrentAgent CurrentAgent = os.environ["agent_type"] global ChatbotHistory ChatbotHistory = [] # timestr = time.strftime("%Y-%m-%d-%H:%M:%S") # # Running_history = Running_history + [(None, 'Timestamp: '+timestr)] # # yield Running_history # WelcomeStr = """ # This is AI Assistant powered by MECH Core Team. # It is connected remotely with GPT4. The following function is available for you. # 1. Free Chat with AI assistant # 2. Search Information and Engineering Data: Vector Database + Internet # 3. Make specific task with tools: # - Text to Sound | Sound to Text | Doc summary # - Code interpret (Beta version) # - Text to Image (forecast) # """ # # Running_history = Running_history + [(None, timestr+'\n'+WelcomeStr)] # ChatbotHistory = ChatbotHistory + [(None, timestr+'\n'+WelcomeStr)] class CodeBlock: ''' CodeBlock Class which is able to run in Code Runner ''' def __init__(self, code): self.code = code self.output = "" self.active_line = None def refresh(self): print(f"Active line: {self.active_line}") print(f"Output: {self.output}") code_1 = """ for i in range(3): print("hello world") """ code_2 = """ !pip install python-pptx """ def Code_Runner(code_raw: str): # interpreter = CodeInterpreter(language="python", debug_mode=True) global CurrentAgent if CurrentAgent == "Zero Short React 2": code_raw = RemoveIndent(code_raw) if '!pip' in code_raw or 'pip install' in code_raw: try: code_raw=code_raw.replace('!pip', 'pip') except Exception as e: print(e) interpreter = CodeInterpreter(language="shell", debug_mode=True) else: interpreter = CodeInterpreter(language="python", debug_mode=True) # interpreter = CodeInterpreter(language=lang, debug_mode=True) code_block = CodeBlock(code_raw) interpreter.active_block = code_block output = interpreter.run() print("Real Output: \n", output) try: if output.strip() =="" or output == []: output = "It is Done. No Error Found." except Exception as e: print(e) return output def RemoveIndent(code_string, indentation_level=4): lines = code_string.split('\n') corrected_lines = [] for line in lines: if line.strip() == "": continue line_without_indentation = line[indentation_level:] \ if line.startswith(' ' * indentation_level) else line corrected_lines.append(line_without_indentation) corrected_content = '\n'.join(corrected_lines) return corrected_content Code_Runner(code_1) Code_Runner(code_2) async def TestCodeInterpret(CustomMessage:str): # create a session session = CodeInterpreterSession(llm=GPTfake) session.start() # generate a response based on user input response = await session.generate_response(CustomMessage) # output the response (text + image) print("AI: ", response.content) for file in response.files: file.show_image() # terminate the session session.stop() ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") sample = ds[0]["audio"] global Audio_output Audio_output = [] def speech_to_text_loc(audio): device = "cpu" pipe = pipeline( "automatic-speech-recognition", model="openai/whisper-small", chunk_length_s=30, device=device, ) print("type of audio:", type(audio)) if type(audio) == dict: text = pipe(audio.copy(), batch_size=2)["text"] else: text = pipe(audio, batch_size=2)["text"] return text print("voice to text loc: ", speech_to_text_loc(sample)) def text_to_speech_loc(text): device = "cpu" pipe = pipeline( "text-to-speech", model="microsoft/speecht5_tts", device=device, ) output = pipe(text) speech = output["audio"] sampling_rate = output["sampling_rate"] print("Type of speech: ", type(speech)) print("sampling_rate: ", sampling_rate) timestr = time.strftime("%Y%m%d-%H%M%S") # sampling_rate = 16000 with open('sample-' + timestr + '.wav', 'wb') as audio: wavfile.write(audio, sampling_rate, speech) # audio = sf.write("convert1.wav", speech, samplerate=16000) print("audio: ", audio) return audio def text_to_speech_loc2(Text_input): global Audio_output processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts") model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts") vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan") inputs = processor(text = Text_input, return_tensors="pt") # load xvector containing speaker's voice characteristics from a dataset embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder) print("Type of speech: ", type(speech)) timestr = time.strftime("%Y%m%d-%H%M%S") # sampling_rate = 16000 with open('sample-' + timestr + '.wav', 'wb') as audio: sf.write(audio, speech.numpy(), samplerate=16000) # audio = sf.write("convert1.wav", speech, samplerate=16000) print("audio: ", audio) Audio_output.append(audio.name) return audio print("text to speech2: ", text_to_speech_loc2("Good morning.")) class GPTRemote(LLM): n: int @property def _llm_type(self) -> str: return "custom" def _call( self, prompt: str, stop: Optional [List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any ) -> str: print("prompt:", prompt) output = asyncio.run(start_playwright(prompt)) # output = "test custom llm" # print(type(output)) if output is None: output = "No Feedback" print("-" * 20) print('Raw: \n', output) keywords = ['Action:', 'Action Input:', 'Observation:', 'Thought:', 'Final Answer:'] # print("Judge 1: ", 'Action:' in output) # print("Judge 2: ", 'Action Input:' in output) # print("Judge 3: ", 'Observation:' in output) # print("Judge 4: ", 'Thought:' in output) # print("Judge Final Answer: ",'Final Answer:' in output) # for item in keywords: # if item in output: # output = output.replace(item, '\n'+item) # if '|' in output: # output = output.replace('|', '') # if 'Thought:' not in output: # output = 'Thought:'+ output # if 'Action Input:' in output and 'Observation:' in output: if 'Action:' in output and 'Observation:' in output: output = output.split('Observation:')[0] global CurrentAgent # if Choice == "Structured Zero Short Agent": if CurrentAgent == 'Structured Zero Short Agent': try: # temp = output.split('{')[1].split('}')[0:-2] if output.strip()[-1] == '}' and 'Action:' in output: print("valid command") elif 'Action:' in output: output = output + '}' print("corrected command") pattern = r'\{((?:[^{}]|(?R))*)\}' temp = regex.search(pattern, output) rrr = temp.group() output = output.replace(rrr, '```'+ '\n' + rrr + '\n'+'```') # print("Found command: ", output) except Exception as e: print("model internal error:", e) print("-" * 20) print("Treated output: \n", output) return output @property def _identifying_params(self) -> Mapping[str, Any]: return [("n", self.n)] def treat_output(text): keywords = ['Action:', 'Action Input:', 'Observation:', 'Thought:', 'Final Answer:'] for item in keywords: if item in text: text.replace(item, '\n'+item) print("treat output: ", text) return text # def _generate( # self, # prompts: List[str], # stop: Optional[List[str]] = None, # run_manager: Optional[CallbackManagerForLLMRun] = None, # **kwargs: Any, # ) -> LLMResult: # result = LLMResult() # result.generations = [Generation("test result")] # return result # """Run the LLM on the given prompts.""" GPTfake = GPTRemote(n=0) async def start_playwright(question: str): start_t = time.time() pw = await async_playwright().start() browser = await pw.chromium.launch(headless=True) end_t = time.time() print("Init Browser Done:", end_t - start_t) start_t = end_t # browser = await pw.webkit.launch(headless=True) page = await browser.new_page() # note all methods are async (use the "await" keyword) await page.goto(os.environ["Endpoint_GPT4"]) # print("Title of Web: ", await page.title()) end_t = time.time() print("New Page Done:", end_t - start_t) start_t = end_t await page.wait_for_timeout(200) # print("Content of Web: ", await page.content()) # print("Test content: ", await page.locator("//div[@class='css-zt5igj e1nzilvr3']").inner_html()) # print("Test content: ", await page.locator("//div[@class='css-zt5igj e1nzilvr3']").inner_text()) await page.locator("//textarea").fill(question) await page.wait_for_timeout(200) # print("Content of Web: ", await page.content()) # await page.locator("//button[@class='css-1wi2cd3 e1d2x3se3']").click() await page.locator("//textarea").press("Enter") await page.wait_for_timeout(200) # print("Content of Web: ", await page.content()) # print("output_text 1", await page.locator("//div[@aria-label='Chat message from assistant']").last.inner_text()) # output_text = await page.locator("//div[@aria-label='Chat message from assistant']").last.inner_text() # print("output_text 1", output_text) output_history = "NOTHING" for i in range(100): output_text_old = await page.locator("//div[@aria-label='Chat message from assistant']").last.inner_text() html_content = await page.locator("//div[@aria-label='Chat message from assistant']//div[@class='stMarkdown']").last.inner_html() markdown_converter = html2text.HTML2Text() output_text = markdown_converter.handle(html_content) print("output_text... :") if output_text == output_history and '▌' not in output_text and output_text != "": end_t = time.time() print("Output Done:", end_t - start_t) return output_text else: await page.wait_for_timeout(500) output_history = output_text print("-------- Final Answer-----------\n", output_text) await browser.close() # import playsound langchain.debug = True global memory3 memory3 = ConversationBufferWindowMemory(memory_key="chat_history", input_key="input", output_key='output', return_messages=True) global memory2 memory2 = ConversationBufferWindowMemory(memory_key="chat_history") global memory_openai memory_openai = ConversationBufferWindowMemory(memory_key="memory", return_messages=True) global last_request last_request = "" # Custom document loaders class MyElmLoader(UnstructuredEmailLoader): """Wrapper to fallback to text/plain when default does not work""" def load(self) -> List[Document]: """Wrapper adding fallback for elm without html""" try: try: doc = UnstructuredEmailLoader.load(self) except ValueError as e: if 'text/html content not found in email' in str(e): # Try plain text self.unstructured_kwargs["content_source"]="text/plain" doc = UnstructuredEmailLoader.load(self) else: raise except Exception as e: # Add file_path to exception message raise type(e)(f"{self.file_path}: {e}") from e return doc LOADER_MAPPING = { ".csv": (CSVLoader, {}), # ".docx": (Docx2txtLoader, {}), ".doc": (UnstructuredWordDocumentLoader, {}), ".docx": (UnstructuredWordDocumentLoader, {}), ".enex": (EverNoteLoader, {}), ".eml": (MyElmLoader, {}), ".epub": (UnstructuredEPubLoader, {}), ".html": (UnstructuredHTMLLoader, {}), ".md": (UnstructuredMarkdownLoader, {}), ".odt": (UnstructuredODTLoader, {}), ".pdf": (PyMuPDFLoader, {}), ".ppt": (UnstructuredPowerPointLoader, {}), ".pptx": (UnstructuredPowerPointLoader, {}), ".txt": (TextLoader, {"encoding": "utf8"}), ".xls": (UnstructuredExcelLoader, {}), ".xlsx": (UnstructuredExcelLoader, {"mode":"elements"}), # Add more mappings for other file extensions and loaders as needed } source_directory = 'Upload Files' global file_list_loaded file_list_loaded = [] chunk_size = 500 chunk_overlap = 300 global file_list_by_user file_list_by_user = [] global Filename_Chatbot Filename_Chatbot = "" def load_single_document(file_path: str) -> List[Document]: ext = "." + file_path.rsplit(".", 1)[-1] if ext in LOADER_MAPPING: loader_class, loader_args = LOADER_MAPPING[ext] loader = loader_class(file_path, **loader_args) return loader.load() raise ValueError(f"Unsupported file extension '{ext}'") def load_documents(source_dir: str, ignored_files: List[str] = []) -> List[Document]: """ Loads all documents from the source documents directory, ignoring specified files """ all_files = [] for ext in LOADER_MAPPING: all_files.extend( glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True) ) filtered_files = [file_path for file_path in all_files if file_path not in ignored_files] with Pool(processes=os.cpu_count()) as pool: results = [] with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar: for i, docs in enumerate(pool.imap_unordered(load_single_document, filtered_files)): results.extend(docs) pbar.update() return results def load_documents_2(all_files: List[str] = [], ignored_files: List[str] = []) -> List[Document]: """ Loads all documents from the source documents directory, ignoring specified files """ # all_files = [] # for ext in LOADER_MAPPING: # all_files.extend( # glob.glob(os.path.join(source_dir, f"**/*{ext}"), recursive=True) # ) filtered_files = [file_path for file_path in all_files if file_path not in ignored_files] results = [] with tqdm(total=len(filtered_files), desc='Loading new documents', ncols=80) as pbar: for file in filtered_files: docs = load_single_document(file) results.extend(docs) pbar.update() return results def process_documents(ignored_files: List[str] = []) -> List[Document]: """ Load documents and split in chunks """ print(f"Loading documents from {source_directory}") documents = load_documents(source_directory, ignored_files) if not documents: print("No new documents to load") exit(0) print(f"Loaded {len(documents)} new documents from {source_directory}") # text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) text_splitter = TokenTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) texts = text_splitter.split_documents(documents) print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)") return texts def process_documents_2(ignored_files: List[str] = []) -> List[Document]: """ Load documents and split in chunks """ global file_list_loaded print(f"Loading documents from {source_directory}") print("File Path to start processing:", file_list_loaded) documents = load_documents_2(file_list_loaded, ignored_files) if not documents: print("No new documents to load") exit(0) print(f"Loaded {len(documents)} new documents from {source_directory}") # text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) text_splitter = TokenTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) texts = text_splitter.split_documents(documents) print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)") return texts def process_documents_3(ignored_files: List[str] = []) -> List[Document]: """ Load documents and split in chunks """ global file_list_loaded print(f"Loading documents from {source_directory}") print("File Path to start processing:", file_list_loaded) documents = load_documents_2(file_list_loaded, ignored_files) if not documents: print("No new documents to load") exit(0) print(f"Loaded {len(documents)} new documents from {source_directory}") # text_splitter = RecursiveCharacterTextSplitter(chunk_size=8000, chunk_overlap=1000) text_splitter = TokenTextSplitter(chunk_size=4000, chunk_overlap=500) texts = text_splitter.split_documents(documents) print(f"Split into {len(texts)} chunks of text (max. {chunk_size} tokens each)") return texts def UpdateDb(): global vectordb_p global index_name # pinecone.Index(index_name).delete(delete_all=True, namespace='') # collection = vectordb_p.get() # split_docs = process_documents([metadata['source'] for metadata in collection['metadatas']]) # split_docs = process_documents() split_docs = process_documents_2() tt = len(split_docs) print(split_docs[tt-1]) print(f"Creating embeddings. May take some minutes...") vectordb_p = Pinecone.from_documents(split_docs, embeddings, index_name = index_name) print("Pinecone Updated Done") print(index.describe_index_stats()) ListAgentWithRemoteGPT = ['Zero Short React 2','Zero Short Agent 2', 'OpenAI Multi 2', 'Conversation Agent', 'Code Interpreter', 'Structured Zero Short Agent'] def SummarizeDoc(): global vectordb_p global Choice global CurrentAgent # pinecone.Index(index_name).delete(delete_all=True, namespace='') # collection = vectordb_p.get() # split_docs = process_documents([metadata['source'] for metadata in collection['metadatas']]) # split_docs = process_documents() split_docs = process_documents_3() tt = len(split_docs) print(split_docs[tt-1]) sum_text="" try: if CurrentAgent in ListAgentWithRemoteGPT: sum_chain = load_summarize_chain(GPTfake, chain_type='refine', verbose=True) else: sum_chain = load_summarize_chain(llm, chain_type='refine', verbose=True) sum_text = sum_chain.run(split_docs) return sum_text except Exception as e: print("SummarizeDoc error:", e) # sum_text = "test sum" class DB_Search(BaseTool): name = "Vector_Database_Search" description = "This is the internal vector database to search information firstly. If information is found, it is trustful." def _run(self, query: str) -> str: response, source = QAQuery_p(query) # response = "test db_search feedback" return response def _arun(self, query: str): raise NotImplementedError("N/A") class DB_Search2(BaseTool): name = "Vector Database Search" description = "This is the internal vector database to search information firstly (i.e. engineering data, acronym.)" def _run(self, query: str) -> str: response, source = QAQuery_p(query) # response = "test db_search feedback" return response def _arun(self, query: str): raise NotImplementedError("N/A") def Text2Sound(text): speech_config = speechsdk.SpeechConfig(subscription=os.environ['SPEECH_KEY'], region=os.environ['SPEECH_REGION']) audio_config = speechsdk.audio.AudioOutputConfig(use_default_speaker=True) speech_config.speech_synthesis_voice_name='en-US-JennyNeural' # speech_synthesizer = "" speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config) speech_synthesis_result = speech_synthesizer.speak_text_async(text).get() # if speech_synthesis_result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted: # print("Speech synthesized for text [{}]".format(text)) # elif speech_synthesis_result.reason == speechsdk.ResultReason.Canceled: # cancellation_details = speech_synthesis_result.cancellation_details # print("Speech synthesis canceled: {}".format(cancellation_details.reason)) # if cancellation_details.reason == speechsdk.CancellationReason.Error: # if cancellation_details.error_details: # print("Error details: {}".format(cancellation_details.error_details)) # print("Did you set the speech resource key and region values?") print("test") return speech_synthesis_result pass def get_azure_access_token(): azure_key = os.environ.get("SPEECH_KEY") try: response = requests.post( "https://eastus.api.cognitive.microsoft.com/sts/v1.0/issuetoken", headers={ "Ocp-Apim-Subscription-Key": azure_key } ) response.raise_for_status() except requests.exceptions.RequestException as e: print(f"Error: {e}") return None # print (response.text) return response.text def text_to_speech_2(text): global Audio_output access_token = get_azure_access_token() voice_name='en-US-AriaNeural' if not access_token: return None try: response = requests.post( "https://eastus.tts.speech.microsoft.com/cognitiveservices/v1", headers={ "Authorization": f"Bearer {access_token}", "Content-Type": "application/ssml+xml", "X-MICROSOFT-OutputFormat": "riff-24khz-16bit-mono-pcm", "User-Agent": "TextToSpeechApp", }, data=f""" {text} """, ) response.raise_for_status() timestr = time.strftime("%Y%m%d-%H%M%S") with open('sample-' + timestr + '.wav', 'wb') as audio: audio.write(response.content) print ("File Name ", audio.name) # print (audio) Audio_output.append(audio.name) # return audio.name return audio except requests.exceptions.RequestException as e: print(f"Error: {e}") return None def speech_to_text(Filename_Audio_input_single): print("Start speech to text ....") access_token = get_azure_access_token() if not access_token: return None try: endpoint = f"https://eastus.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1?language=en-US" headers={ "Authorization": f"Bearer {access_token}", "Content-Type": "audio/wav",} response = requests.post(endpoint, headers=headers, data=open(Filename_Audio_input_single, "rb")) print("Speech to Text Raw: ", response.text) text_from_audio = response.text.split('DisplayText":"')[1].split('"}')[0] # text_from_audio = response.text('DisplayText') print("Speech to Text: ", text_from_audio) return text_from_audio except requests.exceptions.RequestException as e: print(f"Error speech_to_text: {e}") return None Text2Sound_tool = Tool( name = "Text_To_Sound_REST_API", # func = Text2Sound, func = text_to_speech_2, description = "Useful when you need to convert text into sound file." ) Text2Sound_tool2 = Tool( name = "Text To Sound REST API", # func = Text2Sound, func = text_to_speech_2, description = "Useful when you need to convert text into sound file." ) Text2Sound_tool_loc = Tool( name = "Text To Sound API 2", # func = Text2Sound, func = text_to_speech_loc2, description = "Useful when you need to convert text into sound file." ) Wikipedia = WikipediaAPIWrapper() Netsearch = DuckDuckGoSearchRun() Python_REPL = PythonREPL() wikipedia_tool = Tool( name = "Wikipedia_Search", func = Wikipedia.run, description = "Useful to search a topic, country or person when there is no availble information in vector database" ) duckduckgo_tool = Tool( name = "Duckduckgo_Internet_Search", func = Netsearch.run, description = "Useful to search information in internet when it is not available in other tools" ) python_tool = Tool( name = "Python_REPL", func = Python_REPL.run, description = "Useful when you need python script to answer questions. You should input python code." ) wikipedia_tool2 = Tool( name = "Wikipedia Search", func = Wikipedia.run, description = "Useful to search a topic, country or person when there is no availble information in vector database" ) duckduckgo_tool2 = Tool( name = "Duckduckgo Internet Search", func = Netsearch.run, description = "Useful to search in internet for real-time information and additional information which is not available in other tools" ) python_tool2 = Tool( name = "Python REPL", func = Python_REPL.run, description = "Useful when you need python script to answer questions. You should input python code." ) python_tool3 = Tool( name = "Code Runner", func = Code_Runner, description = """Code Interpreter which is able to run code block in local machine.\n It is capable to treat **any** task by running the code and output the result. (i.e. analyzer data, modify/creat documents, draw diagram/flowchart ...)\n You should input detail code with right indentation.""" ) # tools = [DB_Search(), wikipedia_tool, duckduckgo_tool, python_tool] os.environ["OPENAI_API_TYPE"] = "azure" os.environ["OPENAI_API_KEY"] = os.environ["OPENAI_API_KEY"] os.environ["OPENAI_API_BASE"] = os.environ["OPENAI_API_BASE"] os.environ["OPENAI_API_VERSION"] = os.environ["OPENAI_API_VERSION"] # os.environ["OPENAI_API_VERSION"] = "2023-05-15" username = os.environ["username1"] password = os.environ["password"] SysLock = os.environ["SysLock"] # 0=unlock 1=lock # deployment_name="Chattester" chat = AzureChatOpenAI( deployment_name=os.environ["deployment_name"], temperature=0, ) llm = chat # llm = GPTfake llm_math = LLMMathChain.from_llm(llm) llm_math_2 = LLMMathChain.from_llm(GPTfake) math_tool = Tool( name ='Calculator', func = llm_math.run, description ='Useful for when you need to answer questions about math.' ) math_tool_2 = Tool( name ='Calculator', func = llm_math_2.run, description ='Useful for when you need to answer questions about math.' ) # openai tools = [DB_Search(), duckduckgo_tool, python_tool, math_tool, Text2Sound_tool] tools2 = [DB_Search2(), duckduckgo_tool2, wikipedia_tool2, python_tool2, math_tool, Text2Sound_tool2] tools_remote = [DB_Search2(), duckduckgo_tool2, wikipedia_tool2, python_tool3, math_tool_2, Text2Sound_tool_loc] # tools = load_tools(["Vector Database Search","Wikipedia Search","Python REPL","llm-math"], llm=llm) # Openai embedding embeddings_openai = OpenAIEmbeddings(deployment="model_embedding", chunk_size=15) # huggingface embedding model embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2' # device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu' device = 'cpu' embeddings_miniLM = HuggingFaceEmbeddings( model_name=embed_model_id, model_kwargs={'device': device}, ) # embeddings = embeddings_openai embeddings = embeddings_miniLM # embeddings = OpenAIEmbeddings(deployment="model_embedding_2", chunk_size=15) pinecone.init( api_key = os.environ["pinecone_api_key"], # environment='asia-southeast1-gcp-free', environment='us-west4-gcp-free', # openapi_config=openapi_config ) # index_name = 'stla-baby' global index_name index_name = 'stla-back' index = pinecone.Index(index_name) # index.delete(delete_all=True, namespace='') print(pinecone.whoami()) print(index.describe_index_stats()) """ Answer the following questions as best you can with details. You can always use tools to convert text to sound. You must always check internal vector database first and try to answer the question based on the information in internal vector database only. Only when there is no information available from vector database, you can search information by using other tools. You have access to the following tools: Vector Database Search: This is the internal database to search information firstly. If information is found, it is trustful. Duckduckgo Internet Search: Useful to search information in internet when it is not available in other tools. Wikipedia Search: Useful to search a topic, country or person when there is no availble information in vector database Python REPL: Useful when you need python to answer questions. You should input python code. Calculator: Useful for when you need to answer questions about math. Text To Sound: Useful when you need to convert text into sound file.""" PREFIX = """Answer the following questions as best you can with detail information and explanation. You can always use tools to convert text to sound. You must always check vector database first and try to answer the question based on the information in vector database only. Only when there is no information available from vector database, you can search information by using other tools. When the final answer has output files, you must output the **name** of the file. You have access to the following tools:""" PREFIX_2 = """You are a helpful AI assistant. You are capable to handle **any** task. Your mission is to answer the following request as best as you can with detail information and explanation. When you need information, you must always check vector database first and try to answer the question based on the information found in vector database only. Only when there is no information available from vector database, you can search information by using other tools. When the final answer has output files, you must output the **name** of the file.\n ---\n You have access to the following tools:\n""" PREFIX_3 =""" You are a helpful AI assistant. Your mission is to answer the following user request as best as you can with detail information and explanation. If you are not clear about the request, you can ask user for more details and the confirmation. You can provide additional suggestion to user on the request and ask confirmation from user. When you are clear about the request, you can start to answer the request by **writing a plan** firstly. In general, try to **make plans** with as few steps as possible. When you need information, you can use tools as below and merge all gathered information from different tools. When you need to use "Code Runner" for code running, **Always recap the plan between each code block** (you have extreme short-term memory loss, so you need to recap the plan between each message block to retain it). When you send a message containing code to "Code Runner", it will be executed **on the user's machine**. The user has given you **full and complete permission** to execute any code necessary to complete the task. You have full access to control their computer to help them. Code entered into "Code Runner" will be executed **in the users local environment**. If you want to send data between programming languages, save the data to a txt or json. You should finish each step and output the result with the text content. You can access the internet. Run **any code** to achieve the goal, and if at first you don't succeed, try again and again. You can install new packages with pip. Try to install all necessary packages in one command at the beginning. When a user refers to a filename, they're likely referring to an existing file in the directory you're currently in ("Code Runner" executes on the user's machine). When a user refers to a uploaded file, they're likely referring to an existing file in {file_list_by_user} In general, choose packages that have the most universal chance to be already installed and to work across multiple applications. Packages like ffmpeg and pandoc that are well-supported and powerful. Write messages to the user in Markdown. When the final answer has output files, you must output the **name** of the file. You are capable of **any** task. ---\n You have access to the following tools:\n """ FORMAT_INSTRUCTIONS = """Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [Vector Database Search, Duckduckgo Internet Search, Python REPL, Calculator] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question""" FORMAT_INSTRUCTIONS_2 = """Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [Vector Database Search, Duckduckgo Internet Search, Python REPL, Calculator] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question""" ''' When you don't have enough information, you can use tools and you must define **Action** and **Action Input** after **Thought**. ''' FORMAT_INSTRUCTIONS_3 = """ When you don't have enough information, you can use tools and you must use the following format to define **Thought**, **Action** and **Action Input**:\n\ ''' "Thought": you should always think about what to do.\n "Action": the action to take, should be one of [{tool_names}].\n "Action Input": the input to the action.\n "Observation": the result of the action.\n '''If **Thought**, **Action**, **Action Input** is missing in the response of using tools, you must re-write the response.\n ---\n When you are able to provide final answer, you must use the following format to define **Final Answer** after **Thought**:\n\ ''' "Thought": I now know the final answer.\n "Final Answer": the final answer to the original input question.\n '''\n If **Thought**, **Final Answer** is missing in the response of final answer, you must re-write the response.\n\ ---\nExample of using tools:\n\ ```\n Question: what is architecture?\n---\n Thought: I need to check the definition of architecture in Vector Database.\n Action: Vector Database Search\n Action Input: architecture\n ```\n Example of final answer:\n\ ```\n Question: Good morning\n---\n Thought: I need to make a greeting to user.\n Final Answer: Hello, how can I do for you ?\n ```\n """ FORMAT_INSTRUCTIONS_STRUC = """ When it is necessary to use tools and you must use the following format to output "Thought", "Action" (json blob):\n ''' Thought: you should always think about what to do and consider previous and subsequent steps Action: ``` $JSON_BLOB ``` Observation: the result of the action. ''' $JSON_BLOB with the value of the following 2 keys: "action": **Valid value** must be one of [{tool_names}] "action_input": The input for the action If **Thought**, **Action** is missing in the response of using tools, you must re-write the response. ---\n When you are able to provide final answer, you must use the following format to output "Thought", "Action" (json blob):\n ''' Thought: I know what to respond Action: ``` $JSON_BLOB ``` $JSON_BLOB with value of the following 2 keys: "action": "Final Answer" "action_input": Final response to human ''' If **Thought**, **Action** is missing in the response of using tools, you must re-write the response. """ SUFFIX = """ Begin! Request: {input} Thought: {agent_scratchpad}""" SUFFIX2 = """Begin!\n\ {chat_history}\n\ ---\n\ Question: {input}\n\ ---\n\ Thought: {agent_scratchpad}\n\ """ prompt = ZeroShotAgent.create_prompt( tools, prefix=PREFIX, suffix=SUFFIX, # suffix=SUFFIX2, format_instructions=FORMAT_INSTRUCTIONS, input_variables=["input", "agent_scratchpad"] # input_variables=["input", "chat_history", "agent_scratchpad"] ) prompthead_openai_1 = \ """ You are a helpful AI assistant. Your mission is to answer the following request as best as you can with detail information and explanation. You must always check vector database first and try to answer the request based on the information in vector database only. Only when there is no information available from vector database, you can search information by using other tools. """ prompthead_openai_OR = \ """ You are a helpful AI assistant. """ prompthead_openai = \ """ You are a helpful AI assistant to answer the following questions as best as you can with detail information. You must always search information in vector database first and answer the question based on the information in vector database only. Only when there is no information available from vector database, you can search information by using other method. """ prompt_openai = OpenAIMultiFunctionsAgent.create_prompt( system_message = SystemMessage( content = prompthead_openai), # extra_prompt_messages = [MessagesPlaceholder(variable_name="memory")], ) input_variables=["input", "chat_history", "agent_scratchpad"] input_variables_2=["input", "chat_history", "agent_scratchpad", "file_list_by_user"] agent_ZEROSHOT_REACT = initialize_agent(tools2, llm, # agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose = True, handle_parsing_errors = True, max_iterations = int(os.environ["max_iterations"]), early_stopping_method="generate", agent_kwargs={ 'prefix': PREFIX, 'format_instructions': FORMAT_INSTRUCTIONS, 'suffix': SUFFIX, # 'input_variables': input_variables, }, # input_variables = input_variables, # agent_kwargs={ # 'prompt': prompt, # } ) agent_ZEROSHOT_REACT_2 = initialize_agent(tools_remote, GPTfake, # agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose = True, handle_parsing_errors = True, max_iterations = int(os.environ["max_iterations"]), early_stopping_method="generate", memory = memory2, agent_kwargs={ 'prefix': PREFIX_2, 'format_instructions': FORMAT_INSTRUCTIONS_3, 'suffix': SUFFIX2, 'input_variables': input_variables, }, # input_variables = input_variables, # agent_kwargs={ # 'prompt': prompt, # } ) agent_STRUCTURED_ZEROSHOT_REACT = initialize_agent(tools_remote, GPTfake, # agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose = True, handle_parsing_errors = True, max_iterations = int(os.environ["max_iterations"]), early_stopping_method="generate", memory = memory3, agent_kwargs={ 'prefix': PREFIX_3, 'format_instructions': FORMAT_INSTRUCTIONS_STRUC, # 'suffix': SUFFIX2, "memory_prompts": [MessagesPlaceholder(variable_name="chat_history")], 'input_variables': input_variables_2, }, # input_variables = input_variables, # agent_kwargs={ # 'prompt': prompt, # } ) agent_CODE_INTERPRETER = initialize_agent(tools_remote, GPTfake, # agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose = True, handle_parsing_errors = True, max_iterations = int(os.environ["max_iterations"]), early_stopping_method="generate", memory = memory2, agent_kwargs={ 'prefix': PREFIX_2, 'format_instructions': FORMAT_INSTRUCTIONS_3, 'suffix': SUFFIX2, 'input_variables': input_variables, }, # input_variables = input_variables, # agent_kwargs={ # 'prompt': prompt, # } ) agent_CONVERSATION = initialize_agent(tools_remote, GPTfake, # agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose = True, handle_parsing_errors = True, max_iterations = int(os.environ["max_iterations"]), early_stopping_method="generate", memory = memory2, # agent_kwargs={ # 'prefix': PREFIX_2, # 'format_instructions': FORMAT_INSTRUCTIONS_3, # 'suffix': SUFFIX2, # 'input_variables': input_variables, # }, # input_variables = input_variables, # agent_kwargs={ # 'prompt': prompt, # } ) llm_chain = LLMChain(llm=llm, prompt=prompt) llm_chain_2 = LLMChain(llm=GPTfake, prompt=prompt) # print("Test LLM Chain", llm_chain_2({'agent_scratchpad':"", 'input':"what is PDP?"})) # llm_chain_openai = LLMChain(llm=llm, prompt=prompt_openai, verbose=True) agent_core = ZeroShotAgent(llm_chain=llm_chain, tools=tools2, verbose=True) agent_core_2 = ZeroShotAgent(llm_chain=llm_chain_2, tools=tools2, verbose=True) agent_core_openai = OpenAIMultiFunctionsAgent(llm=llm, tools=tools, prompt=prompt_openai, verbose=True) # agent_core_openai_2 = OpenAIMultiFunctionsAgent(llm=GPTfake, tools=tools, prompt=prompt_openai, verbose=True) agent_ZEROSHOT_AGENT = AgentExecutor.from_agent_and_tools( agent=agent_core, tools=tools2, verbose=True, # memory=memory, handle_parsing_errors = True, max_iterations = int(os.environ["max_iterations"]), early_stopping_method="generate", ) agent_ZEROSHOT_AGENT_2 = AgentExecutor.from_agent_and_tools( agent=agent_core_2, tools=tools_remote, verbose=True, # memory=memory, handle_parsing_errors = True, max_iterations = int(os.environ["max_iterations"]), early_stopping_method="generate", ) agent_OPENAI_MULTI = AgentExecutor.from_agent_and_tools( agent=agent_core_openai, tools=tools, verbose=True, # memory=memory_openai, handle_parsing_errors = True, max_iterations = int(os.environ["max_iterations"]), early_stopping_method="generate", ) # agent_OPENAI_MULTI_2 = AgentExecutor.from_agent_and_tools( # agent=agent_core_openai_2, # tools=tools, # verbose=True, # # memory=memory_openai, # handle_parsing_errors = True, # max_iterations = int(os.environ["max_iterations"]), # early_stopping_method="generate", # ) # agent.max_execution_time = int(os.getenv("max_iterations")) # agent.handle_parsing_errors = True # agent.early_stopping_method = "generate" def SetAgent(Choice): global agent global CurrentAgent if Choice =='Zero Short Agent': agent = agent_ZEROSHOT_AGENT print("Set to:", Choice) elif Choice =='Zero Short React': agent = agent_ZEROSHOT_REACT print("Set to:", Choice) elif Choice =='OpenAI Multi': agent = agent_OPENAI_MULTI print("Set to:", Choice) elif Choice =='Zero Short React 2': agent = agent_ZEROSHOT_REACT_2 print("Set to:", Choice) elif Choice =='Zero Short Agent 2': agent = agent_ZEROSHOT_AGENT_2 print("Set to:", Choice) elif Choice == "None": agent = None print("Set to:", Choice) elif Choice =='Conversation Agent': agent = agent_CONVERSATION print("Set to:", Choice) elif Choice =='Code Interpreter': agent = agent_CODE_INTERPRETER print("Set to:", Choice) elif Choice =='Structured Zero Short Agent': agent = agent_STRUCTURED_ZEROSHOT_REACT print("Set to:", Choice) CurrentAgent = Choice return CurrentAgent global agent Choice = os.environ["agent_type"] SetAgent(Choice) # agent = agent_ZEROSHOT_AGENT # print(agent.agent.llm_chain.prompt.template) # print(agent.agent.llm_chain.prompt) global vectordb # vectordb = Chroma(persist_directory='db', embedding_function=embeddings) global vectordb_p vectordb_p = Pinecone.from_existing_index(index_name, embeddings) # loader = DirectoryLoader('./documents', glob='**/*.txt') # documents = loader.load() # text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=200) # split_docs = text_splitter.split_documents(documents) # print(split_docs) # vectordb = Chroma.from_documents(split_docs, embeddings, persist_directory='db') # question = "what is LCDV ?" # rr = vectordb.similarity_search(query=question, k=4) # vectordb.similarity_search(question) # print(type(rr)) # print(rr) def chathmi(message, history1): # response = "I don't know" # print(message) response, source = QAQuery_p(message) time.sleep(0.3) print(history1) yield response # yield history def chathmi2(message, history): global Audio_output try: output = agent.run(message) time.sleep(0.3) response = output yield response print ("response of chatbot:", response) print ("\n") # real_content = response[-1:] # print("real_content", real_content) try: temp = response.split("(sandbox:/")[1] # (sandbox:/sample-20230805-0807.wav) file_name = temp.split(")")[0] print("file_name:", file_name) dis_audio = [] dis_audio.append(file_name) # yield dis_audio yield dis_audio except: pass if len(Audio_output) > 0: # time.sleep(0.5) # yield Audio_output Audio_output = [] print("History: ", history) print("-" * 20) print("-" * 20) except Exception as e: print("error:", e) # yield history # chatbot = gr.Chatbot().style(color_map =("blue", "pink")) # chatbot = gr.Chatbot(color_map =("blue", "pink")) def func_upload_file(files, chat_history2): global file_list_loaded file_list_loaded = [] print(files) for unit in files: file_list_loaded.append(unit.name) # file_list_loaded = files print(file_list_loaded) # print(chat_history) # test_msg = ["Request Upload File into DB", "Operation Ongoing...."] # chat_history.append(test_msg) for file in files: chat_history2 = chat_history2 + [((file.name,), None)] yield chat_history2 if os.environ["SYS_Upload_Enable"] == "1": UpdateDb() test_msg = ["Request Upload File into DB", "Operation Finished"] chat_history2.append(test_msg) yield chat_history2 def Summary_upload_file(files, chat_history2): global file_list_loaded file_list_loaded = [] for unit in files: file_list_loaded.append(unit.name) # file_list_loaded = files print(file_list_loaded) # print(chat_history) # test_msg = ["Request Upload File into DB", "Operation Ongoing...."] # chat_history.append(test_msg) for file in files: chat_history2 = chat_history2 + [((file.name,), None)] yield chat_history2 if os.environ["SYS_Upload_Enable"] == "1": sumtext = SummarizeDoc() test_msg = [None, sumtext] chat_history2.append(test_msg) yield chat_history2 def User_Upload_file(files, chat_history2): global file_list_by_user file_list_by_user = [] for unit in files: file_list_by_user.append(unit.name) # file_list_loaded = files print(file_list_by_user) # print(chat_history) # test_msg = ["Request Upload File into DB", "Operation Ongoing...."] chat_history2 = chat_history2 + [("Updated Files:\n", None)] yield chat_history2 # chat_history.append(test_msg) for file in files: chat_history2 = chat_history2 + [((file.name,), None)] yield chat_history2 class Logger: def __init__(self, filename): self.terminal = sys.stdout self.log = open(filename, "w") def write(self, message): self.terminal.write(message) self.log.write(message) def flush(self): self.terminal.flush() self.log.flush() def isatty(self): return False sys.stdout = Logger("output.log") def read_logs(): sys.stdout.flush() with open("output.log", "r") as f: return f.read() global record record = [] def LinkElement(chatbot_history): ''' Link chatbot display output with other UI ''' global record if record != chatbot_history: last_response = chatbot_history[-1:][1] print("last response:", last_response) record = chatbot_history print(chatbot_history) # print("link element test") else: print("From linkelement: ", chatbot_history) pass def chathmi3(message, history2): global last_request global Filename_Chatbot global agent # global ChatbotHistory print("Input Message:", message) last_request = message history2 = history2 + [(message, None)] yield ["", history2] try: if agent is not None: # response = agent.run(message) temp = agent({'file_list_by_user':file_list_by_user, 'input': message}) response = temp['output'] elif agent is None: response = asyncio.run(start_playwright(message)) time.sleep(0.1) history2 = history2 + [(None, response)] yield ["", history2] print ("response of chatbot:", response) # real_content = response[-1:] # print("real_content", real_content) try: # temp = response.split("(sandbox:/")[1] # (sandbox:/sample-20230805-0807.wav) file_names = CheckFileinResp(response) print("file_name:", file_names) if file_names != []: for file_name in file_names: if file_name != "": history2 = history2 + [(None, (file_name, file_name))] Filename_Chatbot = file_name yield ["", history2] else: print("No File Found in Response") except Exception as e: print("No need to add file in chatbot:", e) except Exception as e: print("chathmi3 error:", e) # history = history + [(message, None)] print("History2 in chathmi3: ", history2) print("-" * 20) print("-" * 20) def CheckFileinResp(response): Filelist = [] try: pattern = r'sample-(?:\d{8})-(?:\d{6})\.wav' result = re.findall(pattern, response) print("wav file in response:", result) for item in result: Filelist.append(item) except Exception as e: print("No wav found:", e) try: pattern = r"(?i)'?([\w./]*\w+\.(?:pptx|docx|doc|xlsx|txt|png|jpg))'?" result = re.findall(pattern, response) # print("Other file in response:", result) for item in result: if '/' in item: item = item.split('/')[-1] Filelist.append(item) print("Other file in response:", item) except Exception as e: print("No other file found:", e) try: listWord = ['(https://example.com/', '(sandbox:/'] for item in listWord: if item in response: file = response.split(item)[-1].split(")")[0] print("File found:", file) Filelist.append(file) else: continue # return "N/A" except Exception as e: # return "N/A" print("no file with", listWord) return Filelist def chathmi4(message, history2): global last_request global Filename_Chatbot global agent # global ChatbotHistory print("Input Message:", message) last_request = message history2 = history2 + [(message, None)] yield ["", history2, gr.update(visible = False), gr.update(visible = True)] # yield ["", history2, "SUBMIT", "STOP"] try: if agent is not None: # response = agent.run(message) temp = agent({'file_list_by_user':file_list_by_user, 'input': message}) response = temp['output'] # print("chathmi4 response:", response) # test callback # temp = [] # for next_token, content in stream(message): # temp = temp + content # history_int = history2 + [(None, temp)] # yield(None, history_int, None, None) elif agent is None: response = asyncio.run(start_playwright(message)) time.sleep(0.1) history2 = history2 + [(None, response)] yield ["", history2, gr.update(visible = True), gr.update(visible = False)] # yield ["", history2, None, None] print ("response of chatbot:", response) # real_content = response[-1:] # print("real_content", real_content) # try: # # temp = response.split("(sandbox:/")[1] # (sandbox:/sample-20230805-0807.wav) # file_name = CheckFileinResp(response) # print("file_name:", file_name) # if file_name != "N/A": # history2 = history2 + [(None, (file_name,))] # Filename_Chatbot = file_name # yield ["", history2, "SUBMIT", "STOP"] # except Exception as e: # print("No need to add file in chatbot:", e) try: # temp = response.split("(sandbox:/")[1] # (sandbox:/sample-20230805-0807.wav) file_names = CheckFileinResp(response) print("file_name:", file_names) if file_names != []: for file_name in file_names: if file_name != "": history2 = history2 + [(None, (file_name, file_name))] Filename_Chatbot = file_name yield ["", history2, "SUBMIT", "STOP"] else: print("No File Found in Response") except Exception as e: print("No need to add file in chatbot:", e) except Exception as e: print("chathmi4 error:", e) # history = history + [(message, None)] print("History2: ", history2) print("-" * 20) print("-" * 20) def chathmi5(message, history2): global last_request global Filename_Chatbot global agent # global ChatbotHistory print("Input Message:", message) last_request = message history2 = history2 + [(message, None)] yield ["", history2, gr.update(visible = False), gr.update(visible = True)] # yield ["", history2, "SUBMIT", "STOP"] try: if agent is not None: # response = agent.run(message) # test callback temp = "" for next_token, content in stream(message): temp = temp + content response = temp history_int = history2 + [(None, temp)] history2 = history_int yield(None, history_int, None, None) elif agent is None: response = asyncio.run(start_playwright(message)) history2 = history2 + [(None, response)] time.sleep(0.1) yield ["", history2, gr.update(visible = True), gr.update(visible = False)] # yield ["", history2, None, None] print ("response of chatbot:", response) # real_content = response[-1:] # print("real_content", real_content) # try: # # temp = response.split("(sandbox:/")[1] # (sandbox:/sample-20230805-0807.wav) # file_name = CheckFileinResp(response) # print("file_name:", file_name) # if file_name != "N/A": # history2 = history2 + [(None, (file_name,))] # Filename_Chatbot = file_name # yield ["", history2, "SUBMIT", "STOP"] # except Exception as e: # print("No need to add file in chatbot:", e) try: # temp = response.split("(sandbox:/")[1] # (sandbox:/sample-20230805-0807.wav) file_names = CheckFileinResp(response) print("file_name:", file_names) if file_names != []: for file_name in file_names: if file_name != "": history2 = history2 + [(None, (file_name, file_name))] Filename_Chatbot = file_name yield ["", history2, "SUBMIT", "STOP"] else: print("No File Found in Response") except Exception as e: print("No need to add file in chatbot:", e) except Exception as e: print("chathmi4 error:", e) # history = history + [(message, None)] print("History2: ", history2) print("-" * 20) print("-" * 20) def chatremote(message, history2): global last_request global Filename_Chatbot print("Input Message:", message) last_request = message history2 = history2 + [(message, None)] yield ["", history2, gr.update(visible = False), gr.update(visible = True)] # yield ["", history2, "SUBMIT", "STOP"] try: # response = agent.run(message) response = asyncio.run(start_playwright(message)) time.sleep(0.1) history2 = history2 + [(None, response)] yield ["", history2, gr.update(visible = True), gr.update(visible = False)] # yield ["", history2, None, None] print ("response of chatbot remote:", response) # real_content = response[-1:] # print("real_content", real_content) try: temp = response.split("(sandbox:/")[1] # (sandbox:/sample-20230805-0807.wav) file_name = temp.split(")")[0] print("file_name:", file_name) history2 = history2 + [(None, (file_name,))] Filename_Chatbot = file_name yield ["", history2, "SUBMIT", "STOP"] except: print("No need to add file in chatbot") except Exception as e: print("chathmi remote error:", e) # history = history + [(message, None)] print("History2: ", history2) print("-" * 20) print("-" * 20) def fake(message, history4): pass def clearall(): global memory2 global memory3 global ChatbotHistory ChatbotHistory = [] try: memory2.clear() memory3.clear() except Exception as e: print("memory error:", e) # memory_openai.clear() global Filename_Chatbot Filename_Chatbot = [] # file_path = "output.log" # if os.path.isfile(file_path): # os.remove(file_path) # with open(file_path, "w") as file: # print(f"File '{file_path}' has been created.") return [[], gr.update(visible=True), gr.update(visible=False), []] def retry(history3): global last_request global Filename_Chatbot print("last_request", last_request) message = last_request history3 = history3 + [(message, None)] yield history3 try: if agent is not None: response = agent.run(message) elif agent is None: response = asyncio.run(start_playwright(message)) time.sleep(0.1) history3 = history3 + [(None, response)] print ("response of chatbot:", response) yield history3 # real_content = response[-1:] # print("real_content", real_content) try: # temp = response.split("(sandbox:/")[1] # (sandbox:/sample-20230805-0807.wav) # file_name = temp.split(")")[0] # print("file_name:", file_name) # history3 = history3 + [(None, (file_name,))] # yield history3 file_names = CheckFileinResp(response) print("file_name:", file_names) if file_names != []: for file_name in file_names: if file_name != "": history2 = history2 + [(None, (file_name, file_name))] Filename_Chatbot = file_name yield history3 else: print("No File Found in Response") except: print("No need to add file in chatbot") except Exception as e: print("Retry error:", e) # yield chathmi3(last_request, chatbot_history) def display_input(message, history2): global last_request print("Input Message:", message) last_request = message history2 = history2 + [(message, None)] return history2 def Inference_Agent(history_inf): global last_request message = last_request try: response = agent.run(message) time.sleep(0.1) history_inf = history_inf + [(None, response)] return ["",history_inf] except Exception as e: print("error:", e) def ClearText(): return "" def playsound1(): global Filename_Chatbot print("playsound1: ", Filename_Chatbot) try: if Filename_Chatbot.split(".")[1] == 'wav': soundfilename = Filename_Chatbot print("soundfilename:", soundfilename) # return None # Filename_Chatbot = "" return gr.update(value = soundfilename) # return soundfilename # yield soundfilename except Exception as e: print("playsound error:", e) return None def playsound2(): global Filename_Chatbot try: if Filename_Chatbot.split(".")[1] == 'wav': soundfilename = Filename_Chatbot print("soundfilename:", soundfilename) # return None # playsound(soundfilename) mixer.init() mixer.music.load(soundfilename) mixer.music.play() except Exception as e: print("playsound2 error:", e) return None def HMI_Runing(): return [gr.update(visible=False), gr.update(visible=True)] def HMI_Wait(): return [gr.update(visible=True), gr.update(visible=False)] def ClearAudio(): print("clear audio ...") return None def Text2Sound_HMI(): global last_answer global Filename_Chatbot global Audio_output print("Last answer in Text2Sound_HMI", last_answer) # text_to_speech_2(last_answer) text_to_speech_loc2(last_answer) Filename_Chatbot = Audio_output[-1] print("Filename_Chatbot in Text2Sound_HMI", Filename_Chatbot) # try: # if Filename_Chatbot.split(".")[1] == 'wav': # soundfilename = Filename_Chatbot # print("soundfilename:", soundfilename) # # return None # return gr.update(value = soundfilename) # # return soundfilename # # yield soundfilename # except Exception as e: # print("playsound error:", e) # return None def UpdateChatbot(Running_history): if Running_history==[]: timestr = time.strftime("%Y-%m-%d-%H:%M:%S") # # Running_history = Running_history + [(None, 'Timestamp: '+timestr)] # # yield Running_history WelcomeStr = """ This is AI Assistant powered by MECH Core Team and it is connected remotely with GPT4. The following function is available for you. 1. Free Chat with AI assistant 2. Search Information and Engineering Data: Vector Database + Internet 3. Make specific task with tools: Text to Sound | Sound to Text | Doc summary 4. Code interpret: very powerful to modify/create/analyze documents (90%) 5. Text to Image | Image to Text: (forecast) """ Running_history = Running_history + [(None, timestr+'\n'+WelcomeStr)] # ChatbotHistory = ChatbotHistory + [(None, timestr+'\n'+WelcomeStr)] yield [Running_history, Running_history] def UpdateChatbot2(Running_history): ''' Not used ''' global ChatbotHistory timestr = time.strftime("%Y-%m-%d-%H:%M:%S") # Running_history = Running_history + [(None, 'Timestamp: '+timestr)] # # yield Running_history WelcomeStr = """ This is AI Assistant powered by MECH Core Team. It is connected remotely with GPT4. The following function is available for you. 1. Free Chat with AI assistant 2. Search Information and Engineering Data: Vector Database + Internet 3. Make specific task with tools: - Text to Sound - Sound to Text - Doc summary - Code interpret (Beta version) - Text to Image (forecast) """ # # Running_history = Running_history + [(None, timestr+'\n'+WelcomeStr)] ChatbotHistory = ChatbotHistory + [(None, timestr+'\n'+WelcomeStr)] yield ChatbotHistory global last_answer last_answer = "" def SingleTalk(WavFile, history5): global last_request global last_answer global Filename_Chatbot ConvertText = speech_to_text_loc(WavFile) last_request = ConvertText # ConvertText = speech_to_text(WavFile) history5 = history5 + [(ConvertText, None)] yield [None, None, history5] message = ConvertText history2 = history5 try: response = agent.run(message) time.sleep(0.1) last_answer = response history2 = history2 + [(None, response)] yield [None, None, history2] # yield ["", history2, None, None] print ("response of chatbot:", response) # real_content = response[-1:] # print("real_content", real_content) try: # file_name = CheckFileinResp(response) # print("file_name:", file_name) # if file_name != "N/A": # history2 = history2 + [(None, (file_name,))] # Filename_Chatbot = file_name # yield [None, None, history2] file_names = CheckFileinResp(response) print("file_name:", file_names) if file_names != []: for file_name in file_names: if file_name != "": history2 = history2 + [(None, (file_name, file_name))] Filename_Chatbot = file_name yield [None, None, history2] except Exception as e: print("No need to add file in chatbot:", e) except Exception as e: print("chathmi3 SingleTalk error:", e) # history = history + [(message, None)] print("History2 in Simple Talk: ", history2) print("-" * 20) print("-" * 20) def vote(data: gr.LikeData): if data.liked: print("You upvoted this response: " + data.value) else: print("You downvoted this response: " + data.value) with gr.Blocks() as demo: # gr.Markdown("Start typing below and then click **SUBMIT** to see the output.") # main = gr.ChatInterface( # fake, # title="STLA BABY - YOUR FRIENDLY GUIDE", # description= "v0.3: Powered by MECH Core Team", # ) # main.textbox.submit(chathmi3, [main.textbox, main.chatbot], [main.textbox, main.chatbot]) UserRecord = gr.State([]) # UserRecord.append() # timestr = time.strftime("%Y-%m-%d-%H:%M:%S") # # Running_history = Running_history + [(None, 'Timestamp: '+timestr)] # # # yield Running_history # WelcomeStr = """ # This is AI Assistant powered by MECH Core Team. # It is connected remotely with GPT4. The following function is available for you. # 1. Free Chat with AI assistant # 2. Search Information and Engineering Data: Vector Database + Internet # 3. Make specific task with tools: # - Text to Sound # - Sound to Text # - Doc summary # - Code interpret (Beta version) # - Text to Image (forecast) # """ # # Running_history = Running_history + [(None, timestr+'\n'+WelcomeStr)] # UserRecord = UserRecord + [(None, timestr+'\n'+WelcomeStr)] # UserRecord.append((None, timestr+'\n'+WelcomeStr)) with gr.Column() as main2: title = gr.Markdown("""#
STLA BABY - YOUR FRIENDLY GUIDE
v0.7.12: Powered by MECH Core Team - GPT4 REMOTE MODE"""), chatbot = gr.Chatbot( # avatar_images=((os.path.join(os.path.dirname(__file__),"User.png")), (os.path.join(os.path.dirname(__file__),"AI.png"))), ) with gr.Row(): AddFile_button = gr.UploadButton("⤴️ File", file_count="multiple", scale= 0, variant="secondary",size='sm') inputtext = gr.Textbox( scale= 4, label="", placeholder = "Input Your Question", show_label = False, ) submit_button = gr.Button("SUBMIT", variant="primary", visible=True) stop_button = gr.Button("STOP", variant='stop', visible=False) with gr.Row(): agentchoice = gr.Dropdown( # choices=['Zero Short Agent','Zero Short React','OpenAI Multi', # 'Zero Short React 2','Zero Short Agent 2','None','Conversation Agent', # 'Code Interpreter', 'Structured Zero Short Agent'], choices=['None','Zero Short React 2','Structured Zero Short Agent'], label="SELECT AI AGENT", scale= 2, show_label = True, value=os.environ["agent_type"], ) voice_input = gr.Audio( source="microphone", type="filepath", scale= 1, label= "INPUT", ) voice_output = gr.Audio( source="microphone", type="filepath", scale= 1, interactive=False, autoplay= True, label= "OUTPUT", ) # with gr.Column(): upload_button = gr.UploadButton("✡️ INGEST DB", file_count="multiple", scale= 0, variant="secondary") summary_file_button = gr.UploadButton("📁 SUM DOC", file_count="multiple", scale= 0, variant="secondary") # with gr.Column(): retry_button = gr.Button("RETRY") clear_button = gr.Button("CLEAR") with gr.Accordion( label = "LOGS", open = False, ): # logs = gr.Textbox() frash_logs = gr.Button("Update Logs ...") logs = gr.Textbox(max_lines = 25) """ GUI Func """ AddFile_button.upload(User_Upload_file, [AddFile_button, chatbot], chatbot) # upload_button.upload(func_upload_file, [upload_button, main.chatbot], main.chatbot) chatbot.like(vote, None, None) retry_button.click(retry, chatbot, chatbot).success(playsound1, None, voice_output).\ success(HMI_Wait, None, [submit_button, stop_button])#.\ # success(ClearAudio, None, voice_output) # inf1 = inputtext.submit(chathmi3, [inputtext, chatbot], [inputtext, chatbot]).\ # then(playsound, None, voice_output) # inf1 = inputtext.submit(chathmi3, [inputtext, chatbot], [inputtext, chatbot]).\ # then(HMI_Runing, None, [submit_button, stop_button]).\ # then(playsound, None, voice_output).\ # then(HMI_Wait, None, [submit_button, stop_button]) # inf4 = inputtext.submit(chathmi4, [inputtext, chatbot], [inputtext, chatbot, submit_button, stop_button]) ''' open ai | new''' # chathmi4 = normal, chathmi5 = callback inf4 = inputtext.submit(chathmi4, [inputtext, chatbot], [inputtext, chatbot, submit_button, stop_button]).\ success(playsound1, None, voice_output, queue=True)#.\ # success(ClearAudio, None, voice_output) ''' Test ''' # inf4 = inputtext.submit(chatremote, [inputtext, chatbot], [inputtext, chatbot, submit_button, stop_button]).\ # success(playsound1, None, voice_output) inf3 = submit_button.click(chathmi3, [inputtext, chatbot], [inputtext, chatbot]).\ success(HMI_Runing, None, [submit_button, stop_button], queue=True).\ success(playsound1, None, voice_output, queue=True).\ success(HMI_Wait, None, [submit_button, stop_button], queue=True)#.\ # success(ClearAudio, None, voice_output) # inf2 = inputtext.submit(display_input, [inputtext, chatbot], chatbot).\ # then(Inference_Agent, chatbot, [inputtext, chatbot]) stop_button.click(read_logs, None, logs, cancels=[inf4,inf3]).\ then(HMI_Wait, None, [submit_button, stop_button], queue=True) # stop_button.click(read_logs, None, logs, cancels=[inf2]) upload_button.upload(func_upload_file, [upload_button, chatbot], chatbot) sum1 = summary_file_button.upload(Summary_upload_file, [summary_file_button, chatbot], chatbot) agentchoice.change(SetAgent, agentchoice, None) frash_logs.click(read_logs, None, logs) voice_input.stop_recording(SingleTalk, [voice_input, chatbot], [voice_input, voice_output, chatbot], queue=True).\ success(Text2Sound_HMI,None,None, queue=True).\ success(playsound1, None, voice_output, queue=True) #.\ # success(HMI_Wait, None, [submit_button, stop_button]).\ # success(ClearAudio, None, voice_output) clear_button.click(clearall, None, [chatbot, submit_button, stop_button], voice_output, cancels=[inf4,inf3,sum1]) # voice_output.end(ClearAudio, None, voice_output) # def clear_voice(): # print("clear audio ...") # voice_output.clear() # voice_output.play(clear_voice, None, None) # demo.load(read_logs, None, logs, every=1) demo.load(UpdateChatbot, UserRecord, [chatbot, UserRecord]) # load(UpdateChatbot, chatbot, chatbot, every=5) # demo(api_name="Update_Chatbot") # demo = gr.Interface( # chathmi, # ["text", "state"], # [chatbot, "state"], # allow_flagging="never", # ) def CreatDb_P(): global vectordb_p index_name = 'stla-baby' loader = DirectoryLoader('./documents', glob='**/*.txt') documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=200) split_docs = text_splitter.split_documents(documents) print(split_docs) pinecone.Index(index_name).delete(delete_all=True, namespace='') vectordb_p = Pinecone.from_documents(split_docs, embeddings, index_name = "stla-baby") print("Pinecone Updated Done") print(index.describe_index_stats()) def QAQuery_p(question: str): global vectordb_p global agent global Choice global CurrentAgent # vectordb = Chroma(persist_directory='db', embedding_function=embeddings) retriever = vectordb_p.as_retriever() retriever.search_kwargs['k'] = int(os.environ["search_kwargs_k"]) # retriever.search_kwargs['fetch_k'] = 100 # if agent == agent_ZEROSHOT_REACT_2 or agent == agent_ZEROSHOT_AGENT_2: if CurrentAgent in ListAgentWithRemoteGPT: print("--------------- QA with Remote --------------") qa = RetrievalQA.from_chain_type(llm=GPTfake, chain_type="stuff", retriever=retriever, return_source_documents = True, verbose = True) else: print("--------------- QA with API --------------") qa = RetrievalQA.from_chain_type(llm=chat, chain_type="stuff", retriever=retriever, return_source_documents = True, verbose = True) # qa = VectorDBQA.from_chain_type(llm=chat, chain_type="stuff", vectorstore=vectordb, return_source_documents=True) # res = qa.run(question) res = qa({"query": question}) print("-" * 20) # print("Question:", question) # print("Answer:", res) # print("Answer:", res['result']) print("-" * 20) # print("Source:", res['source_documents']) response = res['result'] # response = res['source_documents'] source = res['source_documents'] return response, source # def CreatDb(): # ''' # Funtion to creat chromadb DB based on with all docs # ''' # global vectordb # loader = DirectoryLoader('./documents', glob='**/*.txt') # documents = loader.load() # text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=200) # split_docs = text_splitter.split_documents(documents) # print(split_docs) # vectordb = Chroma.from_documents(split_docs, embeddings, persist_directory='db') # vectordb.persist() def QAQuery(question: str): global vectordb # vectordb = Chroma(persist_directory='db', embedding_function=embeddings) retriever = vectordb.as_retriever() retriever.search_kwargs['k'] = 3 # retriever.search_kwargs['fetch_k'] = 100 qa = RetrievalQA.from_chain_type(llm=chat, chain_type="stuff", retriever=retriever, return_source_documents = True) # qa = VectorDBQA.from_chain_type(llm=chat, chain_type="stuff", vectorstore=vectordb, return_source_documents=True) # res = qa.run(question) res = qa({"query": question}) print("-" * 20) print("Question:", question) # print("Answer:", res) print("Answer:", res['result']) print("-" * 20) print("Source:", res['source_documents']) response = res['result'] return response # Used to complete content def completeText(Text): deployment_id="Chattester" prompt = Text completion = openai.Completion.create(deployment_id=deployment_id, prompt=prompt, temperature=0) print(f"{prompt}{completion['choices'][0]['text']}.") # Used to chat def chatText(Text): deployment_id="Chattester" conversation = [{"role": "system", "content": "You are a helpful assistant."}] user_input = Text conversation.append({"role": "user", "content": user_input}) response = openai.ChatCompletion.create(messages=conversation, deployment_id="Chattester") print("\n" + response["choices"][0]["message"]["content"] + "\n") def GUI_launcher(): if SysLock == "1": demo.queue(concurrency_count=3).launch(auth=(username, password), server_name="0.0.0.0", server_port=7860) else: demo.queue(concurrency_count=3).launch(server_name="0.0.0.0", server_port=7860) if __name__ == '__main__': # chatText("what is AI?") # CreatDb() # QAQuery("what is COFOR ?") # CreatDb_P() # QAQuery_p("what is PDP ?") # question = "what is PDP?" # output = asyncio.run(start_playwright(question)) # asyncio.run(TestCodeInterpret('Plot the bitcoin chart of 2023 YTD')) GUI_launcher() # if SysLock == "1": # demo.queue(concurrency_count=3).launch(auth=(username, password), server_name="0.0.0.0", server_port=7860) # else: # demo.queue(concurrency_count=3).launch(server_name="0.0.0.0", server_port=7860) pass