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import streamlit as st
import whisper
import torch
from transformers import pipeline
import spacy
from summa import keywords
import datetime
import os
from pydub import AudioSegment
import concurrent.futures
@st.cache_resource
def load_models():
device = "cuda" if torch.cuda.is_available() else "cpu"
whisper_model = whisper.load_model("small").to(device) # Using 'small' for faster speed
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
nlp = spacy.load("en_core_web_sm")
return whisper_model, summarizer, nlp, device
def split_audio(file_path, chunk_length_ms=60000): # 60 seconds per chunk
audio = AudioSegment.from_file(file_path)
chunks = [audio[i : i + chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]
return chunks
def transcribe_chunk(whisper_model, chunk_path, device):
options = {"fp16": False} if device == "cpu" else {"fp16": True}
return whisper_model.transcribe(chunk_path, **options)["text"]
def extract_action_items(text, nlp):
doc = nlp(text)
actions = []
for sent in doc.sents:
for token in sent:
if token.dep_ == "ROOT" and token.pos_ == "VERB":
action = {
"text": sent.text,
"responsible": [],
"deadline": []
}
for ent in sent.ents:
if ent.label_ == "PERSON":
action["responsible"].append(ent.text)
elif ent.label_ == "DATE":
action["deadline"].append(ent.text)
actions.append(action)
break
return actions
def main():
st.title("🤖 Smart AI Meeting Assistant")
whisper_model, summarizer, nlp, device = load_models()
audio_file = st.file_uploader("Upload meeting audio", type=["wav", "mp3", "m4a", "ogg", "flac"])
if audio_file is not None:
file_path = f"uploaded_audio_{datetime.datetime.now().timestamp()}.wav"
with open(file_path, "wb") as f:
f.write(audio_file.getbuffer())
st.subheader("Meeting Transcription")
with st.spinner("Transcribing audio..."):
chunks = split_audio(file_path)
chunk_paths = []
for i, chunk in enumerate(chunks):
chunk_path = f"chunk_{i}.wav"
chunk.export(chunk_path, format="wav")
chunk_paths.append(chunk_path)
with concurrent.futures.ThreadPoolExecutor() as executor:
transcripts = list(executor.map(lambda cp: transcribe_chunk(whisper_model, cp, device), chunk_paths))
transcript = " ".join(transcripts)
st.write(transcript)
os.remove(file_path)
st.subheader("Meeting Summary")
with st.spinner("Generating summary..."):
truncated_text = transcript[:1024]
summary = summarizer(truncated_text, max_length=150, min_length=50)[0]['summary_text']
st.write(summary)
st.subheader("🚀 Action Items")
actions = extract_action_items(transcript, nlp)
if not actions:
st.write("No action items detected")
else:
for i, action in enumerate(actions, 1):
responsible = ", ".join(action["responsible"]) or "Unassigned"
deadline = ", ".join(action["deadline"]) or "No deadline"
st.markdown(f"""
**Action {i}**
- Task: {action["text"]}
- Responsible: {responsible}
- Deadline: {deadline}
""")
st.subheader("🔑 Key Terms")
key_phrases_result = keywords.keywords(transcript) or ""
key_phrases = [kp.strip() for kp in key_phrases_result.split("\n") if kp.strip()]
st.write(", ".join(key_phrases) if key_phrases else "No key terms extracted")
if __name__ == "__main__":
main()
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