app.py

import streamlit as st
from pyannote.audio import Pipeline
import whisper
import tempfile
import os
import torch
from transformers import pipeline as tf_pipeline
from pydub import AudioSegment
import io

@st.cache_resource
def load_models():
    try:
        # Updated to 3.1 with parameters
        diarization = Pipeline.from_pretrained(
            "pyannote/[email protected]",
            use_auth_token=st.secrets["hf_token"]
        ).instantiate({
            "onset": 0.3,
            "offset": 0.3,
            "min_duration_on": 0.1,
            "min_duration_off": 0.1
        })
        
        transcriber = whisper.load_model("base")
        
        summarizer = tf_pipeline(
            "summarization",
            model="facebook/bart-large-cnn",
            device=0 if torch.cuda.is_available() else -1
        )
        return diarization, transcriber, summarizer
    except Exception as e:
        st.error(f"Error loading models: {str(e)}")
        return None, None, None

def process_audio(audio_file, max_duration=600):
    try:
        audio_bytes = io.BytesIO(audio_file.getvalue())
        
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
            try:
                if audio_file.name.lower().endswith('.mp3'):
                    audio = AudioSegment.from_mp3(audio_bytes)
                else:
                    audio = AudioSegment.from_wav(audio_bytes)
                
                # Standardize format
                audio = audio.set_frame_rate(16000)
                audio = audio.set_channels(1)
                audio = audio.set_sample_width(2)
                
                audio.export(
                    tmp.name,
                    format="wav",
                    parameters=["-ac", "1", "-ar", "16000"]
                )
                tmp_path = tmp.name
                
            except Exception as e:
                st.error(f"Error converting audio: {str(e)}")
                return None

            diarization, transcriber, summarizer = load_models()
            if not all([diarization, transcriber, summarizer]):
                return "Model loading failed"

            with st.spinner("Identifying speakers..."):
                diarization_result = diarization(tmp_path)
            
            with st.spinner("Transcribing audio..."):
                transcription = transcriber.transcribe(tmp_path)
                
            with st.spinner("Generating summary..."):
                summary = summarizer(transcription["text"], max_length=130, min_length=30)

            os.unlink(tmp_path)
            
            return {
                "diarization": diarization_result,
                "transcription": transcription,  # Return full transcription object
                "summary": summary[0]["summary_text"]
            }
            
    except Exception as e:
        st.error(f"Error processing audio: {str(e)}")
        return None

def format_speaker_segments(diarization_result, transcription):
    formatted_segments = []
    audio_duration = transcription.get('duration', 0)
    
    for turn, _, speaker in diarization_result.itertracks(yield_label=True):
        # Skip invalid timestamps
        if turn.start > audio_duration or turn.end > audio_duration:
            continue
            
        # Only add segments with meaningful duration
        if (turn.end - turn.start) >= 0.1:  # 100ms minimum
            formatted_segments.append({
                'speaker': speaker,
                'start': turn.start,
                'end': turn.end,
                'duration': turn.end - turn.start
            })
    
    return formatted_segments

def main():
    st.title("Multi-Speaker Audio Analyzer")
    st.write("Upload an audio file (MP3/WAV) up to 5 minutes long for best performance")

    uploaded_file = st.file_uploader("Choose a file", type=["mp3", "wav"])

    if uploaded_file:
        file_size = len(uploaded_file.getvalue()) / (1024 * 1024)
        st.write(f"File size: {file_size:.2f} MB")
        
        st.audio(uploaded_file, format='audio/wav')
        
        if st.button("Analyze Audio"):
            if file_size > 200:
                st.error("File size exceeds 200MB limit")
            else:
                results = process_audio(uploaded_file)
                
                if results:
                    tab1, tab2, tab3 = st.tabs(["Speakers", "Transcription", "Summary"])
                    
                    with tab1:
                        st.write("Speaker Timeline:")
                        
                        segments = format_speaker_segments(
                            results["diarization"], 
                            results["transcription"]
                        )
                        
                        # Display segments with proper time formatting
                        for segment in segments:
                            col1, col2 = st.columns([2,8])
                            
                            with col1:
                                speaker_num = int(segment['speaker'].split('_')[1])
                                colors = ['🔵', '🔴']  # Simplified to two colors
                                speaker_color = colors[speaker_num % len(colors)]
                                st.write(f"{speaker_color} {segment['speaker']}")
                            
                            with col2:
                                mm_start = int(segment['start'] // 60)
                                ss_start = segment['start'] % 60
                                mm_end = int(segment['end'] // 60)
                                ss_end = segment['end'] % 60
                                
                                time_str = f"{mm_start:02d}:{ss_start:05.2f} → {mm_end:02d}:{ss_end:05.2f}"
                                st.write(time_str)
                            
                            st.markdown("---")
                    
                    with tab2:
                        st.write("Transcription:")
                        st.write(results["transcription"]["text"])
                    
                    with tab3:
                        st.write("Summary:")
                        st.write(results["summary"])

if __name__ == "__main__":
    main()