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import streamlit as st |
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import whisper |
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from transformers import pipeline |
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import spacy |
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from summa import keywords |
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import datetime |
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import os |
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@st.cache_resource |
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def load_models(): |
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whisper_model = whisper.load_model("base") |
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summarizer = pipeline("summarization", model="facebook/bart-large-cnn") |
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nlp = spacy.load("en_core_web_sm") |
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return whisper_model, summarizer, nlp |
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def extract_action_items(text, nlp): |
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doc = nlp(text) |
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actions = [] |
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for sent in doc.sents: |
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for token in sent: |
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if token.dep_ == "ROOT" and token.pos_ == "VERB": |
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action = { |
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"text": sent.text, |
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"responsible": [], |
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"deadline": [] |
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} |
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for ent in sent.ents: |
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if ent.label_ == "PERSON": |
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action["responsible"].append(ent.text) |
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elif ent.label_ == "DATE": |
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action["deadline"].append(ent.text) |
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actions.append(action) |
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break |
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return actions |
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def main(): |
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st.title("🤖 Smart AI Meeting Assistant") |
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whisper_model, summarizer, nlp = load_models() |
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audio_file = st.file_uploader("Upload meeting audio", type=["wav", "mp3", "m4a"]) |
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if audio_file is not None: |
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file_path = f"uploaded_audio_{datetime.datetime.now().timestamp()}" |
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with open(file_path, "wb") as f: |
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f.write(audio_file.getbuffer()) |
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st.subheader("Meeting Transcription") |
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with st.spinner("Transcribing audio..."): |
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result = whisper_model.transcribe(file_path) |
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transcript = result["text"] |
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st.write(transcript) |
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os.remove(file_path) |
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st.subheader("Meeting Summary") |
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with st.spinner("Generating summary..."): |
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truncated_text = transcript[:1024] |
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summary = summarizer(truncated_text, max_length=150, min_length=50)[0]['summary_text'] |
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st.write(summary) |
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st.subheader("🚀 Action Items") |
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actions = extract_action_items(transcript, nlp) |
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if not actions: |
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st.write("No action items detected") |
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else: |
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for i, action in enumerate(actions, 1): |
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responsible = ", ".join(action["responsible"]) or "Unassigned" |
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deadline = ", ".join(action["deadline"]) or "No deadline" |
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st.markdown(f""" |
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**Action {i}** |
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- Task: {action["text"]} |
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- Responsible: {responsible} |
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- Deadline: {deadline} |
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""") |
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st.subheader("🔑 Key Terms") |
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key_phrases_result = keywords.keywords(transcript) or "" |
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key_phrases = [kp.strip() for kp in key_phrases_result.split("\n") if kp.strip()] |
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st.write(", ".join(key_phrases) if key_phrases else "No key terms extracted") |
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if __name__ == "__main__": |
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main() |
