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import gradio as gr
import os
import torch
from pathlib import Path
import whisper
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
def process_meeting(api_key, topic, audio_file):
os.environ["OPENAI_API_KEY"] = api_key
# Set the filename to the uploaded audio file's name without extension
ffname, _ = os.path.splitext(audio_file.name)
audio_file_path = ffname + ".m4a"
# Save the uploaded audio file
with open(audio_file_path, 'wb') as f:
f.write(audio_file.read())
file = audio_file_path
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model = whisper.load_model("medium.en").to(DEVICE)
def transcribe_file(model, file):
file_path = Path(file)
print(f"Transcribing file: {file_path}\n")
result = model.transcribe(file, verbose = False, language = "en")
return result["text"]
transcribed_text = transcribe_file(model, file)
loader = TextLoader(transcribed_text, encoding="utf8")
doc = loader.load()
text_splitter = CharacterTextSplitter(
separator = "\n",
chunk_size = 1024,
chunk_overlap = 50,
length_function = len,
)
llmChainName = "text-davinci-003"
llm = OpenAI(
model_name=llmChainName,
streaming=True,
verbose=True,
temperature=0
)
embeddings = OpenAIEmbeddings()
persist_directory = './chroma'
texts = text_splitter.split_documents(doc)
store = Chroma.from_documents(texts, embeddings, collection_name="meeting", persist_directory=persist_directory)
llm = ChatOpenAI(
model_name="gpt-4",
temperature=0,
streaming=True,
verbose=False,
callbacks=[StreamingStdOutCallbackHandler()]
)
chain_Instructions = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=store.as_retriever(search_kwargs={"k": 1}))
meeting_summary = chain_Instructions.run("Please summarize the meeting and provide the minutes. Include actions, responsible persons, and due dates where possible. Provide a bullet list for actions. The topic is: " + topic)
try:
store.persist()
del store
except Exception as e:
print(e)
return meeting_summary
# Define the Gradio interface
iface = gr.Interface(
fn=process_meeting,
inputs=[
gr.inputs.Textbox(lines=1, label="OpenAI API Key"),
gr.inputs.Textbox(lines=2, label="Meeting Topic"),
gr.inputs.File(label="Upload Audio"),
],
outputs=gr.outputs.Textbox(label="Meeting Minutes"),
live=False,
)
# Launch the app
iface.launch()
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