# Imports import base64 import glob import json import math import openai import os import pytz import re import requests import streamlit as st import textract import time import zipfile import huggingface_hub import dotenv from audio_recorder_streamlit import audio_recorder from bs4 import BeautifulSoup from collections import deque from datetime import datetime from dotenv import load_dotenv from huggingface_hub import InferenceClient from io import BytesIO from langchain.chat_models import ChatOpenAI from langchain.chains import ConversationalRetrievalChain from langchain.embeddings import OpenAIEmbeddings from langchain.memory import ConversationBufferMemory from langchain.text_splitter import CharacterTextSplitter from langchain.vectorstores import FAISS from openai import ChatCompletion from PyPDF2 import PdfReader from templates import bot_template, css, user_template from xml.etree import ElementTree as ET import streamlit.components.v1 as components # Import Streamlit Components for HTML5 st.set_page_config(page_title="๐ŸชLlama Whisperer๐Ÿฆ™ Voice Chat๐ŸŒŸ", layout="wide") def add_Med_Licensing_Exam_Dataset(): import streamlit as st from datasets import load_dataset dataset = load_dataset("augtoma/usmle_step_1")['test'] # Using 'test' split st.title("USMLE Step 1 Dataset Viewer") if len(dataset) == 0: st.write("๐Ÿ˜ข The dataset is empty.") else: st.write(""" ๐Ÿ” Use the search box to filter questions or use the grid to scroll through the dataset. """) # ๐Ÿ‘ฉโ€๐Ÿ”ฌ Search Box search_term = st.text_input("Search for a specific question:", "") # ๐ŸŽ› Pagination records_per_page = 100 num_records = len(dataset) num_pages = max(int(num_records / records_per_page), 1) # Skip generating the slider if num_pages is 1 (i.e., all records fit in one page) if num_pages > 1: page_number = st.select_slider("Select page:", options=list(range(1, num_pages + 1))) else: page_number = 1 # Only one page # ๐Ÿ“Š Display Data start_idx = (page_number - 1) * records_per_page end_idx = start_idx + records_per_page # ๐Ÿงช Apply the Search Filter filtered_data = [] for record in dataset[start_idx:end_idx]: if isinstance(record, dict) and 'text' in record and 'id' in record: if search_term: if search_term.lower() in record['text'].lower(): st.markdown(record) filtered_data.append(record) else: filtered_data.append(record) # ๐ŸŒ Render the Grid for record in filtered_data: st.write(f"## Question ID: {record['id']}") st.write(f"### Question:") st.write(f"{record['text']}") st.write(f"### Answer:") st.write(f"{record['answer']}") st.write("---") st.write(f"๐Ÿ˜Š Total Records: {num_records} | ๐Ÿ“„ Displaying {start_idx+1} to {min(end_idx, num_records)}") # 1. Constants and Top Level UI Variables # My Inference API Copy # API_URL = 'https://qe55p8afio98s0u3.us-east-1.aws.endpoints.huggingface.cloud' # Dr Llama # Original: API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf" API_KEY = os.getenv('API_KEY') MODEL1="meta-llama/Llama-2-7b-chat-hf" MODEL1URL="https://huggingface.co/meta-llama/Llama-2-7b-chat-hf" HF_KEY = os.getenv('HF_KEY') headers = { "Authorization": f"Bearer {HF_KEY}", "Content-Type": "application/json" } key = os.getenv('OPENAI_API_KEY') prompt = f"Write instructions to teach anyone to write a discharge plan. List the entities, features and relationships to CCDA and FHIR objects in boldface." should_save = st.sidebar.checkbox("๐Ÿ’พ Save", value=True, help="Save your session data.") # 2. Prompt label button demo for LLM def add_witty_humor_buttons(): with st.expander("Wit and Humor ๐Ÿคฃ", expanded=True): # Tip about the Dromedary family st.markdown("๐Ÿ”ฌ **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.") # Define button descriptions descriptions = { "Generate Limericks ๐Ÿ˜‚": "Write ten random adult limericks based on quotes that are tweet length and make you laugh ๐ŸŽญ", "Wise Quotes ๐Ÿง™": "Generate ten wise quotes that are tweet length ๐Ÿฆ‰", "Funny Rhymes ๐ŸŽค": "Create ten funny rhymes that are tweet length ๐ŸŽถ", "Medical Jokes ๐Ÿ’‰": "Create ten medical jokes that are tweet length ๐Ÿฅ", "Minnesota Humor โ„๏ธ": "Create ten jokes about Minnesota that are tweet length ๐ŸŒจ๏ธ", "Top Funny Stories ๐Ÿ“–": "Create ten funny stories that are tweet length ๐Ÿ“š", "More Funny Rhymes ๐ŸŽ™๏ธ": "Create ten more funny rhymes that are tweet length ๐ŸŽต" } # Create columns col1, col2, col3 = st.columns([1, 1, 1], gap="small") # Add buttons to columns if col1.button("Generate Limericks ๐Ÿ˜‚"): StreamLLMChatResponse(descriptions["Generate Limericks ๐Ÿ˜‚"]) if col2.button("Wise Quotes ๐Ÿง™"): StreamLLMChatResponse(descriptions["Wise Quotes ๐Ÿง™"]) if col3.button("Funny Rhymes ๐ŸŽค"): StreamLLMChatResponse(descriptions["Funny Rhymes ๐ŸŽค"]) col4, col5, col6 = st.columns([1, 1, 1], gap="small") if col4.button("Medical Jokes ๐Ÿ’‰"): StreamLLMChatResponse(descriptions["Medical Jokes ๐Ÿ’‰"]) if col5.button("Minnesota Humor โ„๏ธ"): StreamLLMChatResponse(descriptions["Minnesota Humor โ„๏ธ"]) if col6.button("Top Funny Stories ๐Ÿ“–"): StreamLLMChatResponse(descriptions["Top Funny Stories ๐Ÿ“–"]) col7 = st.columns(1, gap="small") if col7[0].button("More Funny Rhymes ๐ŸŽ™๏ธ"): StreamLLMChatResponse(descriptions["More Funny Rhymes ๐ŸŽ™๏ธ"]) def SpeechSynthesis(result): documentHTML5=''' Read It Aloud

๐Ÿ”Š Read It Aloud


''' components.html(documentHTML5, width=1280, height=1024) #return result # 3. Stream Llama Response # @st.cache_resource def StreamLLMChatResponse(prompt): try: endpoint_url = API_URL hf_token = API_KEY client = InferenceClient(endpoint_url, token=hf_token) gen_kwargs = dict( max_new_tokens=512, top_k=30, top_p=0.9, temperature=0.2, repetition_penalty=1.02, stop_sequences=["\nUser:", "<|endoftext|>", ""], ) stream = client.text_generation(prompt, stream=True, details=True, **gen_kwargs) report=[] res_box = st.empty() collected_chunks=[] collected_messages=[] allresults='' for r in stream: if r.token.special: continue if r.token.text in gen_kwargs["stop_sequences"]: break collected_chunks.append(r.token.text) chunk_message = r.token.text collected_messages.append(chunk_message) try: report.append(r.token.text) if len(r.token.text) > 0: result="".join(report).strip() res_box.markdown(f'*{result}*') except: st.write('Stream llm issue') SpeechSynthesis(result) return result except: 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).') # 4. Run query with payload def query(payload): response = requests.post(API_URL, headers=headers, json=payload) st.markdown(response.json()) return response.json() def get_output(prompt): return query({"inputs": prompt}) # 5. Auto name generated output files from time and content def generate_filename(prompt, file_type): central = pytz.timezone('US/Central') safe_date_time = datetime.now(central).strftime("%m%d_%H%M") replaced_prompt = prompt.replace(" ", "_").replace("\n", "_") safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:45] return f"{safe_date_time}_{safe_prompt}.{file_type}" # 6. Speech transcription via OpenAI service def transcribe_audio(openai_key, file_path, model): openai.api_key = openai_key OPENAI_API_URL = "https://api.openai.com/v1/audio/transcriptions" headers = { "Authorization": f"Bearer {openai_key}", } with open(file_path, 'rb') as f: data = {'file': f} response = requests.post(OPENAI_API_URL, headers=headers, files=data, data={'model': model}) if response.status_code == 200: st.write(response.json()) chatResponse = chat_with_model(response.json().get('text'), '') # ************************************* transcript = response.json().get('text') filename = generate_filename(transcript, 'txt') response = chatResponse user_prompt = transcript create_file(filename, user_prompt, response, should_save) return transcript else: st.write(response.json()) st.error("Error in API call.") return None # 7. Auto stop on silence audio control for recording WAV files def save_and_play_audio(audio_recorder): audio_bytes = audio_recorder(key='audio_recorder') if audio_bytes: filename = generate_filename("Recording", "wav") with open(filename, 'wb') as f: f.write(audio_bytes) st.audio(audio_bytes, format="audio/wav") return filename return None # 8. File creator that interprets type and creates output file for text, markdown and code def create_file(filename, prompt, response, should_save=True): if not should_save: return base_filename, ext = os.path.splitext(filename) if ext in ['.txt', '.htm', '.md']: with open(f"{base_filename}.md", 'w') as file: try: content = prompt.strip() + '\r\n' + response file.write(content) except: st.write('.') #has_python_code = re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response) #has_python_code = bool(re.search(r"```python([\s\S]*?)```", prompt.strip() + '\r\n' + response)) #if has_python_code: # python_code = re.findall(r"```python([\s\S]*?)```", response)[0].strip() # with open(f"{base_filename}-Code.py", 'w') as file: # file.write(python_code) # with open(f"{base_filename}.md", 'w') as file: # content = prompt.strip() + '\r\n' + response # file.write(content) def truncate_document(document, length): return document[:length] def divide_document(document, max_length): return [document[i:i+max_length] for i in range(0, len(document), max_length)] # 9. Sidebar with UI controls to review and re-run prompts and continue responses @st.cache_resource def get_table_download_link(file_path): with open(file_path, 'r') as file: data = file.read() b64 = base64.b64encode(data.encode()).decode() file_name = os.path.basename(file_path) ext = os.path.splitext(file_name)[1] # get the file extension if ext == '.txt': mime_type = 'text/plain' elif ext == '.py': mime_type = 'text/plain' elif ext == '.xlsx': mime_type = 'text/plain' elif ext == '.csv': mime_type = 'text/plain' elif ext == '.htm': mime_type = 'text/html' elif ext == '.md': mime_type = 'text/markdown' else: mime_type = 'application/octet-stream' # general binary data type href = f'{file_name}' return href def CompressXML(xml_text): root = ET.fromstring(xml_text) for elem in list(root.iter()): if isinstance(elem.tag, str) and 'Comment' in elem.tag: elem.parent.remove(elem) return ET.tostring(root, encoding='unicode', method="xml") # 10. Read in and provide UI for past files @st.cache_resource def read_file_content(file,max_length): if file.type == "application/json": content = json.load(file) return str(content) elif file.type == "text/html" or file.type == "text/htm": content = BeautifulSoup(file, "html.parser") return content.text elif file.type == "application/xml" or file.type == "text/xml": tree = ET.parse(file) root = tree.getroot() xml = CompressXML(ET.tostring(root, encoding='unicode')) return xml elif file.type == "text/markdown" or file.type == "text/md": md = mistune.create_markdown() content = md(file.read().decode()) return content elif file.type == "text/plain": return file.getvalue().decode() else: return "" # 11. Chat with GPT - Caution on quota - now favoring fastest AI pipeline STT Whisper->LLM Llama->TTS @st.cache_resource def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'): model = model_choice conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}] conversation.append({'role': 'user', 'content': prompt}) if len(document_section)>0: conversation.append({'role': 'assistant', 'content': document_section}) start_time = time.time() report = [] res_box = st.empty() collected_chunks = [] collected_messages = [] for chunk in openai.ChatCompletion.create(model='gpt-3.5-turbo', messages=conversation, temperature=0.5, stream=True): collected_chunks.append(chunk) chunk_message = chunk['choices'][0]['delta'] collected_messages.append(chunk_message) content=chunk["choices"][0].get("delta",{}).get("content") try: report.append(content) if len(content) > 0: result = "".join(report).strip() res_box.markdown(f'*{result}*') except: st.write(' ') full_reply_content = ''.join([m.get('content', '') for m in collected_messages]) st.write("Elapsed time:") st.write(time.time() - start_time) return full_reply_content # 12. Embedding VectorDB for LLM query of documents to text to compress inputs and prompt together as Chat memory using Langchain @st.cache_resource def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'): conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}] conversation.append({'role': 'user', 'content': prompt}) if len(file_content)>0: conversation.append({'role': 'assistant', 'content': file_content}) response = openai.ChatCompletion.create(model=model_choice, messages=conversation) return response['choices'][0]['message']['content'] def extract_mime_type(file): if isinstance(file, str): pattern = r"type='(.*?)'" match = re.search(pattern, file) if match: return match.group(1) else: raise ValueError(f"Unable to extract MIME type from {file}") elif isinstance(file, streamlit.UploadedFile): return file.type else: raise TypeError("Input should be a string or a streamlit.UploadedFile object") def extract_file_extension(file): # get the file name directly from the UploadedFile object file_name = file.name pattern = r".*?\.(.*?)$" match = re.search(pattern, file_name) if match: return match.group(1) else: raise ValueError(f"Unable to extract file extension from {file_name}") # Normalize input as text from PDF and other formats @st.cache_resource def pdf2txt(docs): text = "" for file in docs: file_extension = extract_file_extension(file) st.write(f"File type extension: {file_extension}") if file_extension.lower() in ['py', 'txt', 'html', 'htm', 'xml', 'json']: text += file.getvalue().decode('utf-8') elif file_extension.lower() == 'pdf': from PyPDF2 import PdfReader pdf = PdfReader(BytesIO(file.getvalue())) for page in range(len(pdf.pages)): text += pdf.pages[page].extract_text() # new PyPDF2 syntax return text def txt2chunks(text): text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len) return text_splitter.split_text(text) # Vector Store using FAISS @st.cache_resource def vector_store(text_chunks): embeddings = OpenAIEmbeddings(openai_api_key=key) return FAISS.from_texts(texts=text_chunks, embedding=embeddings) # Memory and Retrieval chains @st.cache_resource def get_chain(vectorstore): llm = ChatOpenAI() memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) return ConversationalRetrievalChain.from_llm(llm=llm, retriever=vectorstore.as_retriever(), memory=memory) def process_user_input(user_question): response = st.session_state.conversation({'question': user_question}) st.session_state.chat_history = response['chat_history'] for i, message in enumerate(st.session_state.chat_history): template = user_template if i % 2 == 0 else bot_template st.write(template.replace("{{MSG}}", message.content), unsafe_allow_html=True) filename = generate_filename(user_question, 'txt') response = message.content user_prompt = user_question create_file(filename, user_prompt, response, should_save) def divide_prompt(prompt, max_length): words = prompt.split() chunks = [] current_chunk = [] current_length = 0 for word in words: if len(word) + current_length <= max_length: current_length += len(word) + 1 current_chunk.append(word) else: chunks.append(' '.join(current_chunk)) current_chunk = [word] current_length = len(word) chunks.append(' '.join(current_chunk)) return chunks # 13. Provide way of saving all and deleting all to give way of reviewing output and saving locally before clearing it @st.cache_resource def create_zip_of_files(files): zip_name = "all_files.zip" with zipfile.ZipFile(zip_name, 'w') as zipf: for file in files: zipf.write(file) return zip_name @st.cache_resource def get_zip_download_link(zip_file): with open(zip_file, 'rb') as f: data = f.read() b64 = base64.b64encode(data).decode() href = f'Download All' return href # 14. Inference Endpoints for Whisper (best fastest STT) on NVIDIA T4 and Llama (best fastest AGI LLM) on NVIDIA A10 # My Inference Endpoint #API_URL_IE = f'https://tonpixzfvq3791u9.us-east-1.aws.endpoints.huggingface.cloud' # Original #API_URL_IE = "https://api-inference.huggingface.co/models/openai/whisper-small.en" # A10 Inference Endpoint for whisper large tests API_URL_IE = "https://hifdvffh2em0wn50.us-east-1.aws.endpoints.huggingface.cloud" MODEL2 = "openai/whisper-small.en" MODEL2_URL = "https://huggingface.co/openai/whisper-small.en" #headers = { # "Authorization": "Bearer XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX", # "Content-Type": "audio/wav" #} HF_KEY = os.getenv('HF_KEY') headers = { "Authorization": f"Bearer {HF_KEY}", "Content-Type": "audio/wav" } #@st.cache_resource def query(filename): with open(filename, "rb") as f: data = f.read() response = requests.post(API_URL_IE, headers=headers, data=data) return response.json() def generate_filename(prompt, file_type): central = pytz.timezone('US/Central') safe_date_time = datetime.now(central).strftime("%m%d_%H%M") replaced_prompt = prompt.replace(" ", "_").replace("\n", "_") safe_prompt = "".join(x for x in replaced_prompt if x.isalnum() or x == "_")[:90] return f"{safe_date_time}_{safe_prompt}.{file_type}" # 15. Audio recorder to Wav file def save_and_play_audio(audio_recorder): audio_bytes = audio_recorder() if audio_bytes: filename = generate_filename("Recording", "wav") with open(filename, 'wb') as f: f.write(audio_bytes) st.audio(audio_bytes, format="audio/wav") return filename # 16. Speech transcription to file output def transcribe_audio(filename): output = query(filename) return output def whisper_main(): st.title("Speech to Text") st.write("Record your speech and get the text.") # Audio, transcribe, GPT: filename = save_and_play_audio(audio_recorder) if filename is not None: transcription = transcribe_audio(filename) #try: transcript = transcription['text'] #except: #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).') st.write(transcript) response = StreamLLMChatResponse(transcript) # st.write(response) - redundant with streaming result? filename = generate_filename(transcript, ".txt") create_file(filename, transcript, response, should_save) #st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True) import streamlit as st # Sample function to demonstrate a response, replace with your own logic def StreamMedChatResponse(topic): st.write(f"Showing resources or questions related to: {topic}") def add_multi_system_agent_topics(): with st.expander("Multi-System Agent AI Topics ๐Ÿค–", expanded=True): st.markdown("๐Ÿค– **Explore Multi-System Agent AI Topics**: This section provides a variety of topics related to multi-system agent AI systems.") # Define multi-system agent AI topics and descriptions descriptions = { "Reinforcement Learning ๐ŸŽฎ": "Questions related to reinforcement learning algorithms and applications ๐Ÿ•น๏ธ", "Natural Language Processing ๐Ÿ—ฃ๏ธ": "Questions about natural language processing techniques and chatbot development ๐Ÿ—จ๏ธ", "Multi-Agent Systems ๐Ÿค": "Questions pertaining to multi-agent systems and cooperative AI interactions ๐Ÿค–", "Conversational AI ๐Ÿ—จ๏ธ": "Questions on building conversational AI agents and chatbots for various platforms ๐Ÿ’ฌ", "Distributed AI Systems ๐ŸŒ": "Questions about distributed AI systems and their implementation in networked environments ๐ŸŒ", "AI Ethics and Bias ๐Ÿค”": "Questions related to ethics and bias considerations in AI systems and decision-making ๐Ÿง ", "AI in Healthcare ๐Ÿฅ": "Questions about the application of AI in healthcare and medical diagnosis ๐Ÿฉบ", "AI in Autonomous Vehicles ๐Ÿš—": "Questions on the use of AI in autonomous vehicles and self-driving technology ๐Ÿš—" } # Create columns col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small") # Add buttons to columns if col1.button("Reinforcement Learning ๐ŸŽฎ"): st.write(descriptions["Reinforcement Learning ๐ŸŽฎ"]) StreamLLMChatResponse(descriptions["Reinforcement Learning ๐ŸŽฎ"]) if col2.button("Natural Language Processing ๐Ÿ—ฃ๏ธ"): st.write(descriptions["Natural Language Processing ๐Ÿ—ฃ๏ธ"]) StreamLLMChatResponse(descriptions["Natural Language Processing ๐Ÿ—ฃ๏ธ"]) if col3.button("Multi-Agent Systems ๐Ÿค"): st.write(descriptions["Multi-Agent Systems ๐Ÿค"]) StreamLLMChatResponse(descriptions["Multi-Agent Systems ๐Ÿค"]) if col4.button("Conversational AI ๐Ÿ—จ๏ธ"): st.write(descriptions["Conversational AI ๐Ÿ—จ๏ธ"]) StreamLLMChatResponse(descriptions["Conversational AI ๐Ÿ—จ๏ธ"]) col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small") if col5.button("Distributed AI Systems ๐ŸŒ"): st.write(descriptions["Distributed AI Systems ๐ŸŒ"]) StreamLLMChatResponse(descriptions["Distributed AI Systems ๐ŸŒ"]) if col6.button("AI Ethics and Bias ๐Ÿค”"): st.write(descriptions["AI Ethics and Bias ๐Ÿค”"]) StreamLLMChatResponse(descriptions["AI Ethics and Bias ๐Ÿค”"]) if col7.button("AI in Healthcare ๐Ÿฅ"): st.write(descriptions["AI in Healthcare ๐Ÿฅ"]) StreamLLMChatResponse(descriptions["AI in Healthcare ๐Ÿฅ"]) if col8.button("AI in Autonomous Vehicles ๐Ÿš—"): st.write(descriptions["AI in Autonomous Vehicles ๐Ÿš—"]) StreamLLMChatResponse(descriptions["AI in Autonomous Vehicles ๐Ÿš—"]) # 17. Main def main(): st.title("Try Some Topics:") prompt = f"Write ten funny jokes that are tweet length stories that make you laugh. Show as markdown outline with emojis for each." # Add Wit and Humor buttons # add_witty_humor_buttons() # Calling the function to add the multi-system agent AI topics buttons add_multi_system_agent_topics() example_input = st.text_input("Enter your example text:", value=prompt, help="Enter text to get a response from DromeLlama.") if st.button("Run Prompt With DromeLlama", help="Click to run the prompt."): try: StreamLLMChatResponse(example_input) except: 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).') openai.api_key = os.getenv('OPENAI_KEY') menu = ["txt", "htm", "xlsx", "csv", "md", "py"] choice = st.sidebar.selectbox("Output File Type:", menu) model_choice = st.sidebar.radio("Select Model:", ('gpt-3.5-turbo', 'gpt-3.5-turbo-0301')) user_prompt = st.text_area("Enter prompts, instructions & questions:", '', height=100) collength, colupload = st.columns([2,3]) # adjust the ratio as needed with collength: max_length = st.slider("File section length for large files", min_value=1000, max_value=128000, value=12000, step=1000) with colupload: uploaded_file = st.file_uploader("Add a file for context:", type=["pdf", "xml", "json", "xlsx", "csv", "html", "htm", "md", "txt"]) document_sections = deque() document_responses = {} if uploaded_file is not None: file_content = read_file_content(uploaded_file, max_length) document_sections.extend(divide_document(file_content, max_length)) if len(document_sections) > 0: if st.button("๐Ÿ‘๏ธ View Upload"): st.markdown("**Sections of the uploaded file:**") for i, section in enumerate(list(document_sections)): st.markdown(f"**Section {i+1}**\n{section}") st.markdown("**Chat with the model:**") for i, section in enumerate(list(document_sections)): if i in document_responses: st.markdown(f"**Section {i+1}**\n{document_responses[i]}") else: if st.button(f"Chat about Section {i+1}"): st.write('Reasoning with your inputs...') response = chat_with_model(user_prompt, section, model_choice) st.write('Response:') st.write(response) document_responses[i] = response filename = generate_filename(f"{user_prompt}_section_{i+1}", choice) create_file(filename, user_prompt, response, should_save) st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True) if st.button('๐Ÿ’ฌ Chat'): st.write('Reasoning with your inputs...') user_prompt_sections = divide_prompt(user_prompt, max_length) full_response = '' for prompt_section in user_prompt_sections: response = chat_with_model(prompt_section, ''.join(list(document_sections)), model_choice) full_response += response + '\n' # Combine the responses response = full_response st.write('Response:') st.write(response) filename = generate_filename(user_prompt, choice) create_file(filename, user_prompt, response, should_save) st.sidebar.markdown(get_table_download_link(filename), unsafe_allow_html=True) # Compose a file sidebar of past encounters all_files = glob.glob("*.*") all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 20] # exclude files with short names all_files.sort(key=lambda x: (os.path.splitext(x)[1], x), reverse=True) # sort by file type and file name in descending order if st.sidebar.button("๐Ÿ—‘ Delete All"): for file in all_files: os.remove(file) st.experimental_rerun() if st.sidebar.button("โฌ‡๏ธ Download All"): zip_file = create_zip_of_files(all_files) st.sidebar.markdown(get_zip_download_link(zip_file), unsafe_allow_html=True) file_contents='' next_action='' for file in all_files: col1, col2, col3, col4, col5 = st.sidebar.columns([1,6,1,1,1]) # adjust the ratio as needed with col1: if st.button("๐ŸŒ", key="md_"+file): # md emoji button with open(file, 'r') as f: file_contents = f.read() next_action='md' with col2: st.markdown(get_table_download_link(file), unsafe_allow_html=True) with col3: if st.button("๐Ÿ“‚", key="open_"+file): # open emoji button with open(file, 'r') as f: file_contents = f.read() next_action='open' with col4: if st.button("๐Ÿ”", key="read_"+file): # search emoji button with open(file, 'r') as f: file_contents = f.read() next_action='search' with col5: if st.button("๐Ÿ—‘", key="delete_"+file): os.remove(file) st.experimental_rerun() if len(file_contents) > 0: if next_action=='open': file_content_area = st.text_area("File Contents:", file_contents, height=500) if next_action=='md': st.markdown(file_contents) if next_action=='search': file_content_area = st.text_area("File Contents:", file_contents, height=500) st.write('Reasoning with your inputs...') # new - llama response = StreamLLMChatResponse(file_contents) filename = generate_filename(user_prompt, ".md") create_file(filename, file_contents, response, should_save) SpeechSynthesis(response) # old - gpt #response = chat_with_model(user_prompt, file_contents, model_choice) #filename = generate_filename(file_contents, choice) #create_file(filename, user_prompt, response, should_save) st.experimental_rerun() # Feedback # Step: Give User a Way to Upvote or Downvote feedback = st.radio("Step 8: Give your feedback", ("๐Ÿ‘ Upvote", "๐Ÿ‘Ž Downvote")) if feedback == "๐Ÿ‘ Upvote": st.write("You upvoted ๐Ÿ‘. Thank you for your feedback!") else: st.write("You downvoted ๐Ÿ‘Ž. Thank you for your feedback!") load_dotenv() st.write(css, unsafe_allow_html=True) st.header("Chat with documents :books:") user_question = st.text_input("Ask a question about your documents:") if user_question: process_user_input(user_question) with st.sidebar: st.subheader("Your documents") docs = st.file_uploader("import documents", accept_multiple_files=True) with st.spinner("Processing"): raw = pdf2txt(docs) if len(raw) > 0: length = str(len(raw)) text_chunks = txt2chunks(raw) vectorstore = vector_store(text_chunks) st.session_state.conversation = get_chain(vectorstore) st.markdown('# AI Search Index of Length:' + length + ' Created.') # add timing filename = generate_filename(raw, 'txt') create_file(filename, raw, '', should_save) # 18. Run AI Pipeline if __name__ == "__main__": whisper_main() main() add_Med_Licensing_Exam_Dataset()