MultiAgentSystems/WhisperLlamaMultiAgentSystems
1
1# Imports2import base643import glob4import json5import math6import openai7import os8import pytz9import re10import requests11import streamlit as st12import textract13import time14import zipfile15import huggingface_hub16import dotenv17from audio_recorder_streamlit import audio_recorder18from bs4 import BeautifulSoup19from collections import deque20from datetime import datetime21from dotenv import load_dotenv22from huggingface_hub import InferenceClient23from io import BytesIO24from langchain.chat_models import ChatOpenAI25from langchain.chains import ConversationalRetrievalChain26from langchain.embeddings import OpenAIEmbeddings27from langchain.memory import ConversationBufferMemory28from langchain.text_splitter import CharacterTextSplitter29from langchain.vectorstores import FAISS30from openai import ChatCompletion31from PyPDF2 import PdfReader32from templates import bot_template, css, user_template33from xml.etree import ElementTree as ET34import streamlit.components.v1 as components # Import Streamlit Components for HTML535 36 37st.set_page_config(page_title="🐪Llama Whisperer🦙 Voice Chat🌟", layout="wide")38 39 40def add_Med_Licensing_Exam_Dataset():41 import streamlit as st42 from datasets import load_dataset43 dataset = load_dataset("augtoma/usmle_step_1")['test'] # Using 'test' split44 st.title("USMLE Step 1 Dataset Viewer")45 if len(dataset) == 0:46 st.write("😢 The dataset is empty.")47 else:48 st.write("""49 🔍 Use the search box to filter questions or use the grid to scroll through the dataset.50 """)51 52 # 👩🔬 Search Box53 search_term = st.text_input("Search for a specific question:", "")54 55 # 🎛 Pagination56 records_per_page = 10057 num_records = len(dataset)58 num_pages = max(int(num_records / records_per_page), 1)59 60 # Skip generating the slider if num_pages is 1 (i.e., all records fit in one page)61 if num_pages > 1:62 page_number = st.select_slider("Select page:", options=list(range(1, num_pages + 1)))63 else:64 page_number = 1 # Only one page65 66 # 📊 Display Data67 start_idx = (page_number - 1) * records_per_page68 end_idx = start_idx + records_per_page69 70 # 🧪 Apply the Search Filter71 filtered_data = []72 for record in dataset[start_idx:end_idx]:73 if isinstance(record, dict) and 'text' in record and 'id' in record:74 if search_term:75 if search_term.lower() in record['text'].lower():76 st.markdown(record)77 filtered_data.append(record)78 else:79 filtered_data.append(record)80 81 # 🌐 Render the Grid82 for record in filtered_data:83 st.write(f"## Question ID: {record['id']}")84 st.write(f"### Question:")85 st.write(f"{record['text']}")86 st.write(f"### Answer:")87 st.write(f"{record['answer']}")88 st.write("---")89 90 st.write(f"😊 Total Records: {num_records} | 📄 Displaying {start_idx+1} to {min(end_idx, num_records)}")91 92# 1. Constants and Top Level UI Variables93 94# My Inference API Copy95# API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud' # Dr Llama96# Original:97API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"98API_KEY = os.getenv('API_KEY')99MODEL1="meta-llama/Llama-2-7b-chat-hf"100MODEL1URL="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf"101HF_KEY = os.getenv('HF_KEY')102headers = {103 "Authorization": f"Bearer {HF_KEY}",104 "Content-Type": "application/json"105}106key = os.getenv('OPENAI_API_KEY')107prompt = f"Write instructions to teach anyone to write a discharge plan. List the entities, features and relationships to CCDA and FHIR objects in boldface."108should_save = st.sidebar.checkbox("💾 Save", value=True, help="Save your session data.")109 110# 2. Prompt label button demo for LLM111def add_witty_humor_buttons():112 with st.expander("Wit and Humor 🤣", expanded=True):113 # Tip about the Dromedary family114 st.markdown("🔬 **Fun Fact**: Dromedaries, part of the camel family, have a single hump and are adapted to arid environments. Their 'superpowers' include the ability to survive without water for up to 7 days, thanks to their specialized blood cells and water storage in their hump.")115 116 # Define button descriptions117 descriptions = {118 "Generate Limericks 😂": "Write ten random adult limericks based on quotes that are tweet length and make you laugh 🎭",119 "Wise Quotes 🧙": "Generate ten wise quotes that are tweet length 🦉",120 "Funny Rhymes 🎤": "Create ten funny rhymes that are tweet length 🎶",121 "Medical Jokes 💉": "Create ten medical jokes that are tweet length 🏥",122 "Minnesota Humor ❄️": "Create ten jokes about Minnesota that are tweet length 🌨️",123 "Top Funny Stories 📖": "Create ten funny stories that are tweet length 📚",124 "More Funny Rhymes 🎙️": "Create ten more funny rhymes that are tweet length 🎵"125 }126 127 # Create columns128 col1, col2, col3 = st.columns([1, 1, 1], gap="small")129 130 # Add buttons to columns131 if col1.button("Generate Limericks 😂"):132 StreamLLMChatResponse(descriptions["Generate Limericks 😂"])133 134 if col2.button("Wise Quotes 🧙"):135 StreamLLMChatResponse(descriptions["Wise Quotes 🧙"])136 137 if col3.button("Funny Rhymes 🎤"):138 StreamLLMChatResponse(descriptions["Funny Rhymes 🎤"])139 140 col4, col5, col6 = st.columns([1, 1, 1], gap="small")141 142 if col4.button("Medical Jokes 💉"):143 StreamLLMChatResponse(descriptions["Medical Jokes 💉"])144 145 if col5.button("Minnesota Humor ❄️"):146 StreamLLMChatResponse(descriptions["Minnesota Humor ❄️"])147 148 if col6.button("Top Funny Stories 📖"):149 StreamLLMChatResponse(descriptions["Top Funny Stories 📖"])150 151 col7 = st.columns(1, gap="small")152 153 if col7[0].button("More Funny Rhymes 🎙️"):154 StreamLLMChatResponse(descriptions["More Funny Rhymes 🎙️"])155 156def SpeechSynthesis(result):157 documentHTML5='''158 <!DOCTYPE html>159 <html>160 <head>161 <title>Read It Aloud</title>162 <script type="text/javascript">163 function readAloud() {164 const text = document.getElementById("textArea").value;165 const speech = new SpeechSynthesisUtterance(text);166 window.speechSynthesis.speak(speech);167 }168 </script>169 </head>170 <body>171 <h1>🔊 Read It Aloud</h1>172 <textarea id="textArea" rows="10" cols="80">173 '''174 documentHTML5 = documentHTML5 + result175 documentHTML5 = documentHTML5 + '''176 </textarea>177 <br>178 <button onclick="readAloud()">🔊 Read Aloud</button>179 </body>180 </html>181 '''182 183 components.html(documentHTML5, width=1280, height=1024)184 #return result185 186 187# 3. Stream Llama Response188# @st.cache_resource189def StreamLLMChatResponse(prompt):190 try:191 endpoint_url = API_URL192 hf_token = API_KEY193 client = InferenceClient(endpoint_url, token=hf_token)194 gen_kwargs = dict(195 max_new_tokens=512,196 top_k=30,197 top_p=0.9,198 temperature=0.2,199 repetition_penalty=1.02,200 stop_sequences=["\nUser:", "<|endoftext|>", "</s>"],201 )202 stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs)203 report=[]204 res_box = st.empty()205 collected_chunks=[]206 collected_messages=[]207 allresults=''208 for r in stream:209 if r.token.special:210 continue211 if r.token.text in gen_kwargs["stop_sequences"]:212 break213 collected_chunks.append(r.token.text)214 chunk_message = r.token.text215 collected_messages.append(chunk_message)216 try:217 report.append(r.token.text)218 if len(r.token.text) > 0:219 result="".join(report).strip()220 res_box.markdown(f'*{result}*')221 222 except:223 st.write('Stream llm issue')224 SpeechSynthesis(result)225 return result226 except:227 st.write('Llama model is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).')228 229# 4. Run query with payload230def query(payload):231 response = requests.post(API_URL, headers=headers, json=payload)232 st.markdown(response.json())233 return response.json()234def get_output(prompt):235 return query({"inputs": prompt})236 237# 5. Auto name generated output files from time and content238def generate_filename(prompt, file_type):239 central = pytz.timezone('US/Central')240 safe_date_time = datetime.now(central).strftime("%m%d_%H%M")241 replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")242 safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:45]243 return f"{safe_date_time}_{safe_prompt}.{file_type}"244 245# 6. Speech transcription via OpenAI service246def transcribe_audio(openai_key, file_path, model):247 openai.api_key = openai_key248 OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions"249 headers = {250 "Authorization": f"Bearer {openai_key}",251 }252 with open(file_path, 'rb') as f:253 data = {'file': f}254 response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model})255 if response.status_code == 200:256 st.write(response.json())257 chatResponse = chat_with_model(response.json().get('text'), '') # *************************************258 transcript = response.json().get('text')259 filename = generate_filename(transcript, 'txt')260 response = chatResponse261 user_prompt = transcript262 create_file(filename, user_prompt, response, should_save)263 return transcript264 else:265 st.write(response.json())266 st.error("Error in API call.")267 return None268 269# 7. Auto stop on silence audio control for recording WAV files270def save_and_play_audio(audio_recorder):271 audio_bytes = audio_recorder(key='audio_recorder')272 if audio_bytes:273 filename = generate_filename("Recording", "wav")274 with open(filename, 'wb') as f:275 f.write(audio_bytes)276 st.audio(audio_bytes, format="audio/wav")277 return filename278 return None279 280# 8. File creator that interprets type and creates output file for text, markdown and code281def create_file(filename, prompt, response, should_save=True):282 if not should_save:283 return284 base_filename, ext = os.path.splitext(filename)285 if ext in ['.txt', '.htm', '.md']:286 with open(f"{base_filename}.md", 'w') as file:287 try:288 content = prompt.strip() + '\r\n' + response289 file.write(content)290 except:291 st.write('.')292 293 #has_python_code = re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response)294 #has_python_code = bool(re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response))295 #if has_python_code:296 # python_code = re.findall(r"```python([\s\S]*?)```", response)[0].strip()297 # with open(f"{base_filename}-Code.py", 'w') as file:298 # file.write(python_code)299 # with open(f"{base_filename}.md", 'w') as file:300 # content = prompt.strip() + '\r\n' + response301 # file.write(content)302 303def truncate_document(document, length):304 return document[:length]305def divide_document(document, max_length):306 return [document[i:i+max_length] for i in range(0, len(document), max_length)]307 308# 9. Sidebar with UI controls to review and re-run prompts and continue responses309@st.cache_resource310def get_table_download_link(file_path):311 with open(file_path, 'r') as file:312 data = file.read()313 314 b64 = base64.b64encode(data.encode()).decode() 315 file_name = os.path.basename(file_path)316 ext = os.path.splitext(file_name)[1] # get the file extension317 if ext == '.txt':318 mime_type = 'text/plain'319 elif ext == '.py':320 mime_type = 'text/plain'321 elif ext == '.xlsx':322 mime_type = 'text/plain'323 elif ext == '.csv':324 mime_type = 'text/plain'325 elif ext == '.htm':326 mime_type = 'text/html'327 elif ext == '.md':328 mime_type = 'text/markdown'329 else:330 mime_type = 'application/octet-stream' # general binary data type331 href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'332 return href333 334 335def CompressXML(xml_text):336 root = ET.fromstring(xml_text)337 for elem in list(root.iter()):338 if isinstance(elem.tag, str) and 'Comment' in elem.tag:339 elem.parent.remove(elem)340 return ET.tostring(root, encoding='unicode', method="xml")341 342# 10. Read in and provide UI for past files343@st.cache_resource344def read_file_content(file,max_length):345 if file.type == "application/json":346 content = json.load(file)347 return str(content)348 elif file.type == "text/html" or file.type == "text/htm":349 content = BeautifulSoup(file, "html.parser")350 return content.text351 elif file.type == "application/xml" or file.type == "text/xml":352 tree = ET.parse(file)353 root = tree.getroot()354 xml = CompressXML(ET.tostring(root, encoding='unicode'))355 return xml356 elif file.type == "text/markdown" or file.type == "text/md":357 md = mistune.create_markdown()358 content = md(file.read().decode())359 return content360 elif file.type == "text/plain":361 return file.getvalue().decode()362 else:363 return ""364 365# 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS366@st.cache_resource367def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):368 model = model_choice369 conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]370 conversation.append({'role': 'user', 'content': prompt})371 if len(document_section)>0:372 conversation.append({'role': 'assistant', 'content': document_section})373 start_time = time.time()374 report = []375 res_box = st.empty()376 collected_chunks = []377 collected_messages = []378 for chunk in openai.ChatCompletion.create(model='gpt-3.5-turbo', messages=conversation, temperature=0.5, stream=True):379 collected_chunks.append(chunk) 380 chunk_message = chunk['choices'][0]['delta'] 381 collected_messages.append(chunk_message) 382 content=chunk["choices"][0].get("delta",{}).get("content")383 try:384 report.append(content)385 if len(content) > 0:386 result = "".join(report).strip()387 res_box.markdown(f'*{result}*') 388 except:389 st.write(' ')390 full_reply_content = ''.join([m.get('content', '') for m in collected_messages])391 st.write("Elapsed time:")392 st.write(time.time() - start_time)393 return full_reply_content394 395# 12. Embedding VectorDB for LLM query of documents to text to compress inputs and prompt together as Chat memory using Langchain396@st.cache_resource397def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):398 conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]399 conversation.append({'role': 'user', 'content': prompt})400 if len(file_content)>0:401 conversation.append({'role': 'assistant', 'content': file_content})402 response = openai.ChatCompletion.create(model=model_choice, messages=conversation)403 return response['choices'][0]['message']['content']404 405def extract_mime_type(file):406 if isinstance(file, str):407 pattern = r"type='(.*?)'"408 match = re.search(pattern, file)409 if match:410 return match.group(1)411 else:412 raise ValueError(f"Unable to extract MIME type from {file}")413 elif isinstance(file, streamlit.UploadedFile):414 return file.type415 else:416 raise TypeError("Input should be a string or a streamlit.UploadedFile object")417 418def extract_file_extension(file):419 # get the file name directly from the UploadedFile object420 file_name = file.name421 pattern = r".*?\.(.*?)$"422 match = re.search(pattern, file_name)423 if match:424 return match.group(1)425 else:426 raise ValueError(f"Unable to extract file extension from {file_name}")427 428# Normalize input as text from PDF and other formats429@st.cache_resource430def pdf2txt(docs):431 text = ""432 for file in docs:433 file_extension = extract_file_extension(file)434 st.write(f"File type extension: {file_extension}")435 if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']:436 text += file.getvalue().decode('utf-8')437 elif file_extension.lower() == 'pdf':438 from PyPDF2 import PdfReader439 pdf = PdfReader(BytesIO(file.getvalue()))440 for page in range(len(pdf.pages)):441 text += pdf.pages[page].extract_text() # new PyPDF2 syntax442 return text443 444def txt2chunks(text):445 text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len)446 return text_splitter.split_text(text)447 448# Vector Store using FAISS449@st.cache_resource450def vector_store(text_chunks):451 embeddings = OpenAIEmbeddings(openai_api_key=key)452 return FAISS.from_texts(texts=text_chunks, embedding=embeddings)453 454# Memory and Retrieval chains455@st.cache_resource456def get_chain(vectorstore):457 llm = ChatOpenAI()458 memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)459 return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory)460 461def process_user_input(user_question):462 response = st.session_state.conversation({'question': user_question})463 st.session_state.chat_history = response['chat_history']464 for i, message in enumerate(st.session_state.chat_history):465 template = user_template if i % 2 == 0 else bot_template466 st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True)467 filename = generate_filename(user_question, 'txt')468 response = message.content469 user_prompt = user_question470 create_file(filename, user_prompt, response, should_save) 471 472def divide_prompt(prompt, max_length):473 words = prompt.split()474 chunks = []475 current_chunk = []476 current_length = 0477 for word in words:478 if len(word) + current_length <= max_length:479 current_length += len(word) + 1 480 current_chunk.append(word)481 else:482 chunks.append(' '.join(current_chunk))483 current_chunk = [word]484 current_length = len(word)485 chunks.append(' '.join(current_chunk))486 return chunks487 488 489# 13. Provide way of saving all and deleting all to give way of reviewing output and saving locally before clearing it490 491@st.cache_resource492def create_zip_of_files(files):493 zip_name = "all_files.zip"494 with zipfile.ZipFile(zip_name, 'w') as zipf:495 for file in files:496 zipf.write(file)497 return zip_name498 499@st.cache_resource500def get_zip_download_link(zip_file):501 with open(zip_file, 'rb') as f:502 data = f.read()503 b64 = base64.b64encode(data).decode()504 href = f'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'505 return href506 507# 14. Inference Endpoints for Whisper (best fastest STT) on NVIDIA T4 and Llama (best fastest AGI LLM) on NVIDIA A10508# My Inference Endpoint509#API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud'510# Original511#API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small.en"512# A10 Inference Endpoint for whisper large tests513API_URL_IE = "https://hifdvffh2em0wn50.us-east-1.aws.endpoints.huggingface.cloud"514 515MODEL2 = "openai/whisper-small.en"516MODEL2_URL = "https://huggingface.co/openai/whisper-small.en"517#headers = {518# "Authorization": "Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",519# "Content-Type": "audio/wav"520#}521HF_KEY = os.getenv('HF_KEY')522headers = {523 "Authorization": f"Bearer {HF_KEY}",524 "Content-Type": "audio/wav"525}526 527#@st.cache_resource528def query(filename):529 with open(filename, "rb") as f:530 data = f.read()531 response = requests.post(API_URL_IE, headers=headers, data=data)532 return response.json()533 534def generate_filename(prompt, file_type):535 central = pytz.timezone('US/Central')536 safe_date_time = datetime.now(central).strftime("%m%d_%H%M")537 replaced_prompt = prompt.replace(" ", "_").replace("\n", "_")538 safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90]539 return f"{safe_date_time}_{safe_prompt}.{file_type}"540 541# 15. Audio recorder to Wav file 542def save_and_play_audio(audio_recorder):543 audio_bytes = audio_recorder()544 if audio_bytes:545 filename = generate_filename("Recording", "wav")546 with open(filename, 'wb') as f:547 f.write(audio_bytes)548 st.audio(audio_bytes, format="audio/wav")549 return filename550 551# 16. Speech transcription to file output552def transcribe_audio(filename):553 output = query(filename)554 return output555 556 557def whisper_main():558 st.title("Speech to Text")559 st.write("Record your speech and get the text.")560 561 # Audio, transcribe, GPT:562 filename = save_and_play_audio(audio_recorder)563 if filename is not None:564 transcription = transcribe_audio(filename)565 #try:566 567 transcript = transcription['text']568 #except:569 #st.write('Whisper model is asleep. Starting up now on T4 GPU - please give 5 minutes then retry as it scales up from zero to activate running container(s).')570 571 st.write(transcript)572 response = StreamLLMChatResponse(transcript)573 # st.write(response) - redundant with streaming result?574 filename = generate_filename(transcript, ".txt")575 create_file(filename, transcript, response, should_save)576 #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)577 578import streamlit as st579 580# Sample function to demonstrate a response, replace with your own logic581def StreamMedChatResponse(topic):582 st.write(f"Showing resources or questions related to: {topic}")583 584def add_multi_system_agent_topics():585 with st.expander("Multi-System Agent AI Topics 🤖", expanded=True):586 st.markdown("🤖 **Explore Multi-System Agent AI Topics**: This section provides a variety of topics related to multi-system agent AI systems.")587 588 # Define multi-system agent AI topics and descriptions589 descriptions = {590 "Reinforcement Learning 🎮": "Questions related to reinforcement learning algorithms and applications 🕹️",591 "Natural Language Processing 🗣️": "Questions about natural language processing techniques and chatbot development 🗨️",592 "Multi-Agent Systems 🤝": "Questions pertaining to multi-agent systems and cooperative AI interactions 🤖",593 "Conversational AI 🗨️": "Questions on building conversational AI agents and chatbots for various platforms 💬",594 "Distributed AI Systems 🌐": "Questions about distributed AI systems and their implementation in networked environments 🌐",595 "AI Ethics and Bias 🤔": "Questions related to ethics and bias considerations in AI systems and decision-making 🧠",596 "AI in Healthcare 🏥": "Questions about the application of AI in healthcare and medical diagnosis 🩺",597 "AI in Autonomous Vehicles 🚗": "Questions on the use of AI in autonomous vehicles and self-driving technology 🚗"598 }599 600 # Create columns601 col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small")602 603 # Add buttons to columns604 if col1.button("Reinforcement Learning 🎮"):605 st.write(descriptions["Reinforcement Learning 🎮"])606 StreamLLMChatResponse(descriptions["Reinforcement Learning 🎮"])607 608 if col2.button("Natural Language Processing 🗣️"):609 st.write(descriptions["Natural Language Processing 🗣️"])610 StreamLLMChatResponse(descriptions["Natural Language Processing 🗣️"])611 612 if col3.button("Multi-Agent Systems 🤝"):613 st.write(descriptions["Multi-Agent Systems 🤝"])614 StreamLLMChatResponse(descriptions["Multi-Agent Systems 🤝"])615 616 if col4.button("Conversational AI 🗨️"):617 st.write(descriptions["Conversational AI 🗨️"])618 StreamLLMChatResponse(descriptions["Conversational AI 🗨️"])619 620 col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small")621 622 if col5.button("Distributed AI Systems 🌐"):623 st.write(descriptions["Distributed AI Systems 🌐"])624 StreamLLMChatResponse(descriptions["Distributed AI Systems 🌐"])625 626 if col6.button("AI Ethics and Bias 🤔"):627 st.write(descriptions["AI Ethics and Bias 🤔"])628 StreamLLMChatResponse(descriptions["AI Ethics and Bias 🤔"])629 630 if col7.button("AI in Healthcare 🏥"):631 st.write(descriptions["AI in Healthcare 🏥"])632 StreamLLMChatResponse(descriptions["AI in Healthcare 🏥"])633 634 if col8.button("AI in Autonomous Vehicles 🚗"):635 st.write(descriptions["AI in Autonomous Vehicles 🚗"])636 StreamLLMChatResponse(descriptions["AI in Autonomous Vehicles 🚗"])637 638 639# 17. Main640def main():641 642 st.title("Try Some Topics:")643 prompt = f"Write ten funny jokes that are tweet length stories that make you laugh. Show as markdown outline with emojis for each."644 645 # Add Wit and Humor buttons646 # add_witty_humor_buttons()647 # Calling the function to add the multi-system agent AI topics buttons648 add_multi_system_agent_topics()649 650 example_input = st.text_input("Enter your example text:", value=prompt, help="Enter text to get a response from DromeLlama.")651 if st.button("Run Prompt With DromeLlama", help="Click to run the prompt."):652 try:653 StreamLLMChatResponse(example_input)654 except:655 st.write('DromeLlama is asleep. Starting up now on A10 - please give 5 minutes then retry as KEDA scales up from zero to activate running container(s).')656 657 openai.api_key = os.getenv('OPENAI_KEY')658 menu = ["txt", "htm", "xlsx", "csv", "md", "py"]659 choice = st.sidebar.selectbox("Output File Type:", menu)660 model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301')) 661 user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100)662 collength, colupload = st.columns([2,3]) # adjust the ratio as needed663 with collength:664 max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000)665 with colupload:666 uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"])667 document_sections = deque()668 document_responses = {}669 if uploaded_file is not None:670 file_content = read_file_content(uploaded_file, max_length)671 document_sections.extend(divide_document(file_content, max_length))672 if len(document_sections) > 0:673 if st.button("👁️ View Upload"):674 st.markdown("**Sections of the uploaded file:**")675 for i, section in enumerate(list(document_sections)):676 st.markdown(f"**Section {i+1}**\n{section}")677 st.markdown("**Chat with the model:**")678 for i, section in enumerate(list(document_sections)):679 if i in document_responses:680 st.markdown(f"**Section {i+1}**\n{document_responses[i]}")681 else:682 if st.button(f"Chat about Section {i+1}"):683 st.write('Reasoning with your inputs...')684 response = chat_with_model(user_prompt, section, model_choice)685 st.write('Response:')686 st.write(response)687 document_responses[i] = response688 filename = generate_filename(f"{user_prompt}_section_{i+1}", choice)689 create_file(filename, user_prompt, response, should_save)690 st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)691 if st.button('💬 Chat'):692 st.write('Reasoning with your inputs...')693 user_prompt_sections = divide_prompt(user_prompt, max_length)694 full_response = ''695 for prompt_section in user_prompt_sections:696 response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice)697 full_response += response + '\n' # Combine the responses698 response = full_response699 st.write('Response:')700 st.write(response)701 filename = generate_filename(user_prompt, choice)702 create_file(filename, user_prompt, response, should_save)703 st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True)704 705 # Compose a file sidebar of past encounters706 all_files = glob.glob("*.*")707 all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20] # exclude files with short names708 all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order709 if st.sidebar.button("🗑 Delete All"):710 for file in all_files:711 os.remove(file)712 st.experimental_rerun()713 if st.sidebar.button("⬇️ Download All"):714 zip_file = create_zip_of_files(all_files)715 st.sidebar.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True)716 file_contents=''717 next_action=''718 for file in all_files:719 col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1]) # adjust the ratio as needed720 with col1:721 if st.button("🌐", key="md_"+file): # md emoji button722 with open(file, 'r') as f:723 file_contents = f.read()724 next_action='md'725 with col2:726 st.markdown(get_table_download_link(file), unsafe_allow_html=True)727 with col3:728 if st.button("📂", key="open_"+file): # open emoji button729 with open(file, 'r') as f:730 file_contents = f.read()731 next_action='open'732 with col4:733 if st.button("🔍", key="read_"+file): # search emoji button734 with open(file, 'r') as f:735 file_contents = f.read()736 next_action='search'737 with col5:738 if st.button("🗑", key="delete_"+file):739 os.remove(file)740 st.experimental_rerun()741 742 743 if len(file_contents) > 0:744 if next_action=='open':745 file_content_area = st.text_area("File Contents:", file_contents, height=500)746 if next_action=='md':747 st.markdown(file_contents)748 if next_action=='search':749 file_content_area = st.text_area("File Contents:", file_contents, height=500)750 st.write('Reasoning with your inputs...')751 752 # new - llama753 response = StreamLLMChatResponse(file_contents)754 filename = generate_filename(user_prompt, ".md")755 create_file(filename, file_contents, response, should_save)756 SpeechSynthesis(response)757 758 # old - gpt759 #response = chat_with_model(user_prompt, file_contents, model_choice)760 #filename = generate_filename(file_contents, choice)761 #create_file(filename, user_prompt, response, should_save)762 763 st.experimental_rerun()764 765 # Feedback766 # Step: Give User a Way to Upvote or Downvote767 feedback = st.radio("Step 8: Give your feedback", ("👍 Upvote", "👎 Downvote"))768 if feedback == "👍 Upvote":769 st.write("You upvoted 👍. Thank you for your feedback!")770 else:771 st.write("You downvoted 👎. Thank you for your feedback!")772 773 load_dotenv()774 st.write(css, unsafe_allow_html=True)775 st.header("Chat with documents :books:")776 user_question = st.text_input("Ask a question about your documents:")777 if user_question:778 process_user_input(user_question)779 with st.sidebar:780 st.subheader("Your documents")781 docs = st.file_uploader("import documents", accept_multiple_files=True)782 with st.spinner("Processing"):783 raw = pdf2txt(docs)784 if len(raw) > 0:785 length = str(len(raw))786 text_chunks = txt2chunks(raw)787 vectorstore = vector_store(text_chunks)788 st.session_state.conversation = get_chain(vectorstore)789 st.markdown('# AI Search Index of Length:' + length + ' Created.') # add timing790 filename = generate_filename(raw, 'txt')791 create_file(filename, raw, '', should_save)792 793# 18. Run AI Pipeline794if __name__ == "__main__":795 whisper_main()796 main()797 add_Med_Licensing_Exam_Dataset()