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:
        diarization = Pipeline.from_pretrained(
            "pyannote/speaker-diarization",
            use_auth_token=st.secrets["hf_token"]
        )
        transcriber = whisper.load_model("base")  # Changed from turbo to base as it's more stable
        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):  # limit to 5 minutes initially
    try:
        # First, read the uploaded file into BytesIO
        audio_bytes = io.BytesIO(audio_file.getvalue())
        
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
            try:
                # Convert audio to standard format
                if audio_file.name.lower().endswith('.mp3'):
                    audio = AudioSegment.from_mp3(audio_bytes)
                else:
                    audio = AudioSegment.from_wav(audio_bytes)
                
                # Standardize audio format
                audio = audio.set_frame_rate(16000)  # Set sample rate to 16kHz
                audio = audio.set_channels(1)        # Convert to mono
                audio = audio.set_sample_width(2)    # Set to 16-bit
                
                # Export with specific parameters
                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

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

            # Process with progress bar
            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)

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

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:
        # Display file info
        file_size = len(uploaded_file.getvalue()) / (1024 * 1024)  # Convert to MB
        st.write(f"File size: {file_size:.2f} MB")
        
        # Display audio player
        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:
                    # Display results in tabs
                    tab1, tab2, tab3 = st.tabs(["Speakers", "Transcription", "Summary"])
                    
                    with tab1:
                        st.write("Speaker Segments:")
                        for turn, _, speaker in results["diarization"].itertracks(yield_label=True):
                            st.write(f"{speaker}: {turn.start:.1f}s → {turn.end:.1f}s")
                    
                    with tab2:
                        st.write("Transcription:")
                        st.write(results["transcription"])
                    
                    with tab3:
                        st.write("Summary:")
                        st.write(results["summary"])

if __name__ == "__main__":
    main()