Bootcamp Week 2: Add Meeting Minutes Creator with transcription and generation features
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Mohamed Salah
301
week3/community-contributions/salah/meeting_minutes_v2.py
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301
week3/community-contributions/salah/meeting_minutes_v2.py
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#!/usr/bin/env python3
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import os
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import torch
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import requests
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import json
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import librosa
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import numpy as np
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from pathlib import Path
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from datetime import datetime
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from transformers import pipeline
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import gradio as gr
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# Basic config
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TRANSCRIPTION_MODEL = "openai/whisper-tiny.en"
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OLLAMA_MODEL = "llama3.2:latest"
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OLLAMA_URL = "http://localhost:11434"
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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OUTPUT_DIR = Path("./output")
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# ============================
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# MODEL LOADING
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# ============================
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def check_ollama():
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try:
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response = requests.get(f"{OLLAMA_URL}/api/tags", timeout=5)
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if response.status_code == 200:
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models = response.json().get('models', [])
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model_names = [model['name'] for model in models]
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return OLLAMA_MODEL in model_names
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return False
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except:
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return False
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def call_ollama(prompt):
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payload = {
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"model": OLLAMA_MODEL,
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"prompt": prompt,
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"stream": False,
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"options": {
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"temperature": 0.7,
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"num_predict": 1000
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}
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}
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try:
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response = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
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if response.status_code == 200:
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return response.json().get('response', '').strip()
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return "Error: Ollama request failed"
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except:
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return "Error: Could not connect to Ollama"
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def load_models():
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print("Loading models...")
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if not check_ollama():
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print("Ollama not available")
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return None, False
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try:
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transcription_pipe = pipeline(
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"automatic-speech-recognition",
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model=TRANSCRIPTION_MODEL,
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torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
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device=0 if DEVICE == "cuda" else -1,
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return_timestamps=True
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)
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print("Models loaded successfully")
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return transcription_pipe, True
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except Exception as e:
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print(f"Failed to load models: {e}")
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return None, False
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# ============================
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# PROCESSING FUNCTIONS
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# ============================
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def transcribe_audio(audio_file_path, transcription_pipe):
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if not os.path.exists(audio_file_path):
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return "Error: Audio file not found"
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try:
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# Load audio with librosa
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audio, sr = librosa.load(audio_file_path, sr=16000)
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if not isinstance(audio, np.ndarray):
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audio = np.array(audio)
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result = transcription_pipe(audio)
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# Extract text from result
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if isinstance(result, dict):
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if "text" in result:
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transcription = result["text"].strip()
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elif "chunks" in result:
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transcription = " ".join([chunk["text"] for chunk in result["chunks"]]).strip()
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else:
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transcription = str(result).strip()
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else:
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transcription = str(result).strip()
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return transcription
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except Exception as e:
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return f"Error: {str(e)}"
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def generate_minutes(transcription):
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prompt = f"""Create meeting minutes from this transcript:
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{transcription[:2000]}
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Include:
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- Summary with attendees and topics
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- Key discussion points
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- Important decisions
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- Action items
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Meeting Minutes:"""
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result = call_ollama(prompt)
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return result
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def save_results(transcription, minutes, meeting_type="meeting"):
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try:
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OUTPUT_DIR.mkdir(exist_ok=True)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"{meeting_type}_minutes_{timestamp}.md"
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filepath = OUTPUT_DIR / filename
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content = f"""# Meeting Minutes
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**Generated:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
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## Meeting Minutes
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{minutes}
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## Full Transcription
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{transcription}
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"""
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with open(filepath, 'w', encoding='utf-8') as f:
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f.write(content)
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return str(filepath)
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except Exception as e:
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return f"Error saving: {str(e)}"
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# ============================
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# GRADIO INTERFACE
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# ============================
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def process_audio_file(audio_file, meeting_type, progress=gr.Progress()):
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progress(0.0, desc="Starting...")
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if not hasattr(process_audio_file, 'models') or not process_audio_file.models[0]:
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return "", "", "Models not loaded"
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transcription_pipe, ollama_ready = process_audio_file.models
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if not ollama_ready:
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return "", "", "Ollama not available"
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try:
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audio_path = audio_file.name if hasattr(audio_file, 'name') else str(audio_file)
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if not audio_path:
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return "", "", "No audio file provided"
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progress(0.2, desc="Transcribing...")
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transcription = transcribe_audio(audio_path, transcription_pipe)
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if transcription.startswith("Error:"):
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return transcription, "", "Transcription failed"
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progress(0.6, desc="Generating minutes...")
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minutes = generate_minutes(transcription)
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if minutes.startswith("Error:"):
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return transcription, minutes, "Minutes generation failed"
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progress(0.9, desc="Saving...")
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save_path = save_results(transcription, minutes, meeting_type)
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progress(1.0, desc="Complete!")
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status = f"""Processing completed!
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Transcription: {len(transcription)} characters
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Minutes: {len(minutes)} characters
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Saved to: {save_path}
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Models used:
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- Transcription: {TRANSCRIPTION_MODEL}
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- LLM: {OLLAMA_MODEL}
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- Device: {DEVICE}
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"""
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return transcription, minutes, status
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except Exception as e:
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progress(1.0, desc="Failed")
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return "", "", f"Processing failed: {str(e)}"
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def create_interface():
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with gr.Blocks(title="Meeting Minutes Creator") as interface:
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gr.HTML("<h1>Meeting Minutes Creator</h1><p>HuggingFace Whisper + Ollama</p>")
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with gr.Row():
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with gr.Column():
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gr.Markdown("### Audio Input")
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audio_input = gr.Audio(
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label="Upload or Record Audio",
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type="filepath",
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sources=["upload", "microphone"]
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)
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meeting_type = gr.Dropdown(
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choices=["meeting", "standup", "interview", "call"],
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value="meeting",
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label="Meeting Type"
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)
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process_btn = gr.Button("Generate Minutes", variant="primary")
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gr.HTML(f"""
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<div>
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<h4>Configuration</h4>
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<ul>
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<li>Transcription: {TRANSCRIPTION_MODEL}</li>
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<li>LLM: {OLLAMA_MODEL}</li>
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<li>Device: {DEVICE}</li>
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</ul>
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</div>
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""")
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with gr.Column():
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gr.Markdown("### Results")
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status_output = gr.Markdown("Ready to process audio")
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with gr.Tabs():
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with gr.Tab("Meeting Minutes"):
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minutes_output = gr.Markdown("Minutes will appear here")
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with gr.Tab("Transcription"):
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transcription_output = gr.Textbox(
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"Transcription will appear here",
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lines=15,
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show_copy_button=True
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)
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process_btn.click(
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fn=process_audio_file,
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inputs=[audio_input, meeting_type],
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outputs=[transcription_output, minutes_output, status_output],
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show_progress=True
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)
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return interface
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# ============================
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# MAIN APPLICATION
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# ============================
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def main():
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print("Meeting Minutes Creator - HuggingFace + Ollama")
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print("Loading models...")
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transcription_pipe, ollama_ready = load_models()
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if not transcription_pipe or not ollama_ready:
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print("Failed to load models or connect to Ollama")
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print("Make sure Ollama is running and has the model available")
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return
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process_audio_file.models = (transcription_pipe, ollama_ready)
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print("Models loaded successfully!")
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print("Starting web interface...")
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print("Access at: http://localhost:7860")
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interface = create_interface()
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try:
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interface.launch(
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server_name="localhost",
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server_port=7860,
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debug=False
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)
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except KeyboardInterrupt:
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print("Shutting down...")
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except Exception as e:
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print(f"Failed to launch: {e}")
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if __name__ == "__main__":
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main()
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