LlamaWhisperer / backup2.app.py
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# 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="πŸͺLlama WhispererπŸ¦™ Voice Chat🌟", 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("😒 The dataset is empty.")
else:
st.write("""
πŸ” Use the search box to filter questions or use the grid to scroll through the dataset.
""")
# πŸ‘©β€πŸ”¬ Search Box
search_term = st.text_input("Search for a specific question:", "")
# πŸŽ› 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
# πŸ“Š Display Data
start_idx = (page_number - 1) * records_per_page
end_idx = start_idx + records_per_page
# πŸ§ͺ 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)
# 🌐 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"😊 Total Records: {num_records} | πŸ“„ 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 discharge planning along with guidelines and patient education. List entities, features and relationships to CCDA and FHIR objects in boldface."
should_save = st.sidebar.checkbox("πŸ’Ύ 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 🀣", expanded=True):
# Tip about the Dromedary family
st.markdown("πŸ”¬ **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 πŸ˜‚": "Write ten random adult limericks based on quotes that are tweet length and make you laugh 🎭",
"Wise Quotes πŸ§™": "Generate ten wise quotes that are tweet length πŸ¦‰",
"Funny Rhymes 🎀": "Create ten funny rhymes that are tweet length 🎢",
"Medical Jokes πŸ’‰": "Create ten medical jokes that are tweet length πŸ₯",
"Minnesota Humor ❄️": "Create ten jokes about Minnesota that are tweet length 🌨️",
"Top Funny Stories πŸ“–": "Create ten funny stories that are tweet length πŸ“š",
"More Funny Rhymes πŸŽ™οΈ": "Create ten more funny rhymes that are tweet length 🎡"
}
# Create columns
col1, col2, col3 = st.columns([1, 1, 1], gap="small")
# Add buttons to columns
if col1.button("Wise Limericks πŸ˜‚"):
StreamLLMChatResponse(descriptions["Generate Limericks πŸ˜‚"])
if col2.button("Wise Quotes πŸ§™"):
StreamLLMChatResponse(descriptions["Wise Quotes πŸ§™"])
#if col3.button("Funny Rhymes 🎀"):
# StreamLLMChatResponse(descriptions["Funny Rhymes 🎀"])
col4, col5, col6 = st.columns([1, 1, 1], gap="small")
if col4.button("Top Ten Funniest Clean Jokes πŸ’‰"):
StreamLLMChatResponse(descriptions["Top Ten Funniest Clean Jokes πŸ’‰"])
if col5.button("Minnesota Humor ❄️"):
StreamLLMChatResponse(descriptions["Minnesota Humor ❄️"])
if col6.button("Origins of Medical Science True Stories"):
StreamLLMChatResponse(descriptions["Origins of Medical Science True Stories"])
col7 = st.columns(1, gap="small")
if col7[0].button("Top Ten Best Write a streamlit python program prompts to build AI programs. πŸŽ™οΈ"):
StreamLLMChatResponse(descriptions["Top Ten Best Write a streamlit python program prompts to build AI programs. πŸŽ™οΈ"])
def SpeechSynthesis(result):
documentHTML5='''
<!DOCTYPE html>
<html>
<head>
<title>Read It Aloud</title>
<script type="text/javascript">
function readAloud() {
const text = document.getElementById("textArea").value;
const speech = new SpeechSynthesisUtterance(text);
window.speechSynthesis.speak(speech);
}
</script>
</head>
<body>
<h1>πŸ”Š Read It Aloud</h1>
<textarea id="textArea" rows="10" cols="80">
'''
documentHTML5 = documentHTML5 + result
documentHTML5 = documentHTML5 + '''
</textarea>
<br>
<button onclick="readAloud()">πŸ”Š Read Aloud</button>
</body>
</html>
'''
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|>", "</s>"],
)
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 == "_")[:255] # 255 is linux max, 260 is windows max
#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'
elif ext == '.wav':
mime_type = 'audio/wav'
else:
mime_type = 'application/octet-stream' # general binary data type
href = f'<a href="data:{mime_type};base64,{b64}" target="_blank" download="{file_name}">{file_name}</a>'
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'<a href="data:application/zip;base64,{b64}" download="{zip_file}">Download All</a>'
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"
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')
HF_KEY = st.secrets['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']
st.write(transcript)
response = StreamLLMChatResponse(transcript)
filename_txt = generate_filename(transcript, ".txt")
create_file(filename_txt, transcript, response, should_save)
filename_wav = filename_txt.replace('.txt', '.wav')
import shutil
shutil.copyfile(filename, filename_wav)
if os.path.exists(filename):
os.remove(filename)
except:
st.write('Starting Whisper Model on GPU. Please retry in 30 seconds.')
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_medical_exam_buttons():
with st.expander("Medical Licensing Exam Topics πŸ“š", expanded=False):
st.markdown("🩺 **Important**: This section provides a variety of medical topics that are often encountered in medical licensing exams.")
# Define medical exam terminology descriptions
descriptions = {
"Ultrasound with Doppler 🌊": "3 Questions and Answers with emojis about Doppler Ultrasound imaging techniques πŸŽ₯",
"Oseltamivir 🦠": "3 Questions and Answers with emojis about the antiviral medication Oseltamivir πŸ’Š",
"IM Epinephrine πŸ’‰": "3 Questions and Answers with emojis about intramuscular administration of epinephrine πŸ’ͺ",
"Hypokalemia 🍌": "3 Questions and Answers with emojis about low potassium levels in blood 🩸",
"Succinylcholine πŸ’Š": "3 Questions and Answers with emojis on the use and side-effects of Succinylcholine πŸš‘",
"Phosphoinositol System 🧬": "3 Questions and Answers with emojis about the Phosphoinositol signalling system πŸ› ",
"Ramipril πŸ’Š": "3 Questions and Answers with emojis related to the ACE inhibitor Ramipril 🩺"
}
# Create columns
col1, col2, col3, col4 = st.columns([1, 1, 1, 1], gap="small")
# Add buttons to columns
if col1.button("Ultrasound with Doppler 🌊"):
StreamLLMChatResponse(descriptions["Ultrasound with Doppler 🌊"])
if col2.button("Oseltamivir 🦠"):
StreamLLMChatResponse(descriptions["Oseltamivir 🦠"])
if col3.button("IM Epinephrine πŸ’‰"):
StreamLLMChatResponse(descriptions["IM Epinephrine πŸ’‰"])
if col4.button("Hypokalemia 🍌"):
StreamLLMChatResponse(descriptions["Hypokalemia 🍌"])
col5, col6, col7, col8 = st.columns([1, 1, 1, 1], gap="small")
if col5.button("Succinylcholine πŸ’Š"):
StreamLLMChatResponse(descriptions["Succinylcholine πŸ’Š"])
if col6.button("Phosphoinositol System 🧬"):
StreamLLMChatResponse(descriptions["Phosphoinositol System 🧬"])
if col7.button("Ramipril πŸ’Š"):
StreamLLMChatResponse(descriptions["Ramipril πŸ’Š"])
# 17. Main
def main():
#st.title("GAIA - Medical License Exam Testing")
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()
add_medical_exam_buttons()
with st.expander("Prompts πŸ“š", expanded=False):
example_input = st.text_input("Enter your prompt text for Llama:", value=prompt, help="Enter text to get a response from DromeLlama.")
if st.button("Run Prompt With Llama model", help="Click to run the prompt."):
try:
response=StreamLLMChatResponse(example_input)
create_file(filename, example_input, response, should_save)
except:
st.write('Llama model is asleep. Starting now on A10 GPU. Please wait one minute then retry. KEDA triggered.')
openai.api_key = os.getenv('OPENAI_API_KEY')
if openai.api_key == None: openai.api_key = st.secrets['OPENAI_API_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("πŸ‘οΈ 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('πŸ’¬ 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 markdown md files:
all_files = glob.glob("*.md")
all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10] # 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("πŸ—‘ Delete All Text"):
for file in all_files:
os.remove(file)
st.experimental_rerun()
if st.sidebar.button("⬇️ 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("🌐", 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("πŸ“‚", key="open_"+file): # open emoji button
with open(file, 'r') as f:
file_contents = f.read()
next_action='open'
with col4:
if st.button("πŸ”", key="read_"+file): # search emoji button
with open(file, 'r') as f:
file_contents = f.read()
next_action='search'
with col5:
if st.button("πŸ—‘", 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()
# Function to encode file to base64
def get_base64_encoded_file(file_path):
with open(file_path, "rb") as file:
return base64.b64encode(file.read()).decode()
# Function to create a download link
def get_audio_download_link(file_path):
base64_file = get_base64_encoded_file(file_path)
return f'<a href="data:file/wav;base64,{base64_file}" download="{os.path.basename(file_path)}">⬇️ Download Audio</a>'
# Compose a file sidebar of past encounters
all_files = glob.glob("*.wav")
all_files = [file for file in all_files if len(os.path.splitext(file)[0]) >= 10] # 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
filekey = 'delall'
if st.sidebar.button("πŸ—‘ Delete All Audio", key=filekey):
for file in all_files:
os.remove(file)
st.experimental_rerun()
for file in all_files:
col1, col2 = st.sidebar.columns([6, 1]) # adjust the ratio as needed
with col1:
st.markdown(file)
if st.button("🎡", key="play_" + file): # play emoji button
audio_file = open(file, 'rb')
audio_bytes = audio_file.read()
st.audio(audio_bytes, format='audio/wav')
#st.markdown(get_audio_download_link(file), unsafe_allow_html=True)
#st.text_input(label="", value=file)
with col2:
if st.button("πŸ—‘", key="delete_" + file):
os.remove(file)
st.experimental_rerun()
# Feedback
# Step: Give User a Way to Upvote or Downvote
with st.expander("Give your feedback πŸ‘", expanded=False):
feedback = st.radio("Step 8: Give your feedback", ("πŸ‘ Upvote", "πŸ‘Ž Downvote"))
if feedback == "πŸ‘ Upvote":
st.write("You upvoted πŸ‘. Thank you for your feedback!")
else:
st.write("You downvoted πŸ‘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()