diff --git a/week3/community-contributions/salah/meeting_minutes_v2.py b/week3/community-contributions/salah/meeting_minutes_v2.py
new file mode 100644
index 0000000..2a1adc2
--- /dev/null
+++ b/week3/community-contributions/salah/meeting_minutes_v2.py
@@ -0,0 +1,301 @@
+#!/usr/bin/env python3
+
+import os
+import torch
+import requests
+import json
+import librosa
+import numpy as np
+from pathlib import Path
+from datetime import datetime
+from transformers import pipeline
+import gradio as gr
+
+# Basic config
+TRANSCRIPTION_MODEL = "openai/whisper-tiny.en"
+OLLAMA_MODEL = "llama3.2:latest"
+OLLAMA_URL = "http://localhost:11434"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+OUTPUT_DIR = Path("./output")
+
+# ============================
+# MODEL LOADING
+# ============================
+
+def check_ollama():
+ try:
+ response = requests.get(f"{OLLAMA_URL}/api/tags", timeout=5)
+ if response.status_code == 200:
+ models = response.json().get('models', [])
+ model_names = [model['name'] for model in models]
+ return OLLAMA_MODEL in model_names
+ return False
+ except:
+ return False
+
+def call_ollama(prompt):
+ payload = {
+ "model": OLLAMA_MODEL,
+ "prompt": prompt,
+ "stream": False,
+ "options": {
+ "temperature": 0.7,
+ "num_predict": 1000
+ }
+ }
+
+ try:
+ response = requests.post(f"{OLLAMA_URL}/api/generate", json=payload, timeout=120)
+ if response.status_code == 200:
+ return response.json().get('response', '').strip()
+ return "Error: Ollama request failed"
+ except:
+ return "Error: Could not connect to Ollama"
+
+def load_models():
+ print("Loading models...")
+
+ if not check_ollama():
+ print("Ollama not available")
+ return None, False
+
+ try:
+ transcription_pipe = pipeline(
+ "automatic-speech-recognition",
+ model=TRANSCRIPTION_MODEL,
+ torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
+ device=0 if DEVICE == "cuda" else -1,
+ return_timestamps=True
+ )
+ print("Models loaded successfully")
+ return transcription_pipe, True
+ except Exception as e:
+ print(f"Failed to load models: {e}")
+ return None, False
+
+# ============================
+# PROCESSING FUNCTIONS
+# ============================
+
+def transcribe_audio(audio_file_path, transcription_pipe):
+ if not os.path.exists(audio_file_path):
+ return "Error: Audio file not found"
+
+ try:
+ # Load audio with librosa
+ audio, sr = librosa.load(audio_file_path, sr=16000)
+ if not isinstance(audio, np.ndarray):
+ audio = np.array(audio)
+
+ result = transcription_pipe(audio)
+
+ # Extract text from result
+ if isinstance(result, dict):
+ if "text" in result:
+ transcription = result["text"].strip()
+ elif "chunks" in result:
+ transcription = " ".join([chunk["text"] for chunk in result["chunks"]]).strip()
+ else:
+ transcription = str(result).strip()
+ else:
+ transcription = str(result).strip()
+
+ return transcription
+
+ except Exception as e:
+ return f"Error: {str(e)}"
+
+def generate_minutes(transcription):
+ prompt = f"""Create meeting minutes from this transcript:
+
+{transcription[:2000]}
+
+Include:
+- Summary with attendees and topics
+- Key discussion points
+- Important decisions
+- Action items
+
+Meeting Minutes:"""
+
+ result = call_ollama(prompt)
+ return result
+
+def save_results(transcription, minutes, meeting_type="meeting"):
+ try:
+ OUTPUT_DIR.mkdir(exist_ok=True)
+ timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+ filename = f"{meeting_type}_minutes_{timestamp}.md"
+ filepath = OUTPUT_DIR / filename
+
+ content = f"""# Meeting Minutes
+
+**Generated:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
+
+## Meeting Minutes
+
+{minutes}
+
+## Full Transcription
+
+{transcription}
+"""
+
+ with open(filepath, 'w', encoding='utf-8') as f:
+ f.write(content)
+
+ return str(filepath)
+
+ except Exception as e:
+ return f"Error saving: {str(e)}"
+
+# ============================
+# GRADIO INTERFACE
+# ============================
+
+def process_audio_file(audio_file, meeting_type, progress=gr.Progress()):
+ progress(0.0, desc="Starting...")
+
+ if not hasattr(process_audio_file, 'models') or not process_audio_file.models[0]:
+ return "", "", "Models not loaded"
+
+ transcription_pipe, ollama_ready = process_audio_file.models
+
+ if not ollama_ready:
+ return "", "", "Ollama not available"
+
+ try:
+ audio_path = audio_file.name if hasattr(audio_file, 'name') else str(audio_file)
+ if not audio_path:
+ return "", "", "No audio file provided"
+
+ progress(0.2, desc="Transcribing...")
+ transcription = transcribe_audio(audio_path, transcription_pipe)
+
+ if transcription.startswith("Error:"):
+ return transcription, "", "Transcription failed"
+
+ progress(0.6, desc="Generating minutes...")
+ minutes = generate_minutes(transcription)
+
+ if minutes.startswith("Error:"):
+ return transcription, minutes, "Minutes generation failed"
+
+ progress(0.9, desc="Saving...")
+ save_path = save_results(transcription, minutes, meeting_type)
+
+ progress(1.0, desc="Complete!")
+
+ status = f"""Processing completed!
+
+Transcription: {len(transcription)} characters
+Minutes: {len(minutes)} characters
+Saved to: {save_path}
+
+Models used:
+- Transcription: {TRANSCRIPTION_MODEL}
+- LLM: {OLLAMA_MODEL}
+- Device: {DEVICE}
+"""
+
+ return transcription, minutes, status
+
+ except Exception as e:
+ progress(1.0, desc="Failed")
+ return "", "", f"Processing failed: {str(e)}"
+
+def create_interface():
+ with gr.Blocks(title="Meeting Minutes Creator") as interface:
+
+ gr.HTML("Meeting Minutes Creator
HuggingFace Whisper + Ollama
")
+
+ with gr.Row():
+ with gr.Column():
+ gr.Markdown("### Audio Input")
+
+ audio_input = gr.Audio(
+ label="Upload or Record Audio",
+ type="filepath",
+ sources=["upload", "microphone"]
+ )
+
+ meeting_type = gr.Dropdown(
+ choices=["meeting", "standup", "interview", "call"],
+ value="meeting",
+ label="Meeting Type"
+ )
+
+ process_btn = gr.Button("Generate Minutes", variant="primary")
+
+ gr.HTML(f"""
+
+
Configuration
+
+ - Transcription: {TRANSCRIPTION_MODEL}
+ - LLM: {OLLAMA_MODEL}
+ - Device: {DEVICE}
+
+