app.py

import gradio as gr
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import time
import numpy as np

# --- Configuration ---
device = "cuda:0" if torch.cuda.is_available() else "cpu"
print(f"Device set to use: {device}")

stt_model_id = "openai/whisper-tiny"
summarizer_model_id = "sshleifer/distilbart-cnn-6-6"
SUMMARY_INTERVAL = 30.0  # Time between summarization updates

# --- Load Models ---
print("Loading Speech-to-Text (STT) model...")
stt_model = AutoModelForSpeechSeq2Seq.from_pretrained(stt_model_id).to(device)
processor = AutoProcessor.from_pretrained(stt_model_id)

stt_pipeline = pipeline(
    "automatic-speech-recognition",
    model=stt_model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    generate_kwargs={"max_new_tokens": 128},  # ✅ FIXED: Moved max_new_tokens
    chunk_length_s=30,
    batch_size=16,
    device=device,
)

print("Loading Summarization model...")
summarizer = pipeline("summarization", model=summarizer_model_id, device=device)

def format_summary_as_bullets(summary_text):
    """Format summary into bullet points."""
    if not summary_text:
        return ""
    sentences = summary_text.replace(". ", ".\n- ").split('\n')
    return "- " + "\n".join(sentences).strip()

def process_audio_stream(new_chunk_tuple, accumulated_transcript_state, last_summary_time_state, current_summary_state):
    """Process streaming audio into transcript and summary."""
    if new_chunk_tuple is None:
        return accumulated_transcript_state, current_summary_state, accumulated_transcript_state, last_summary_time_state, current_summary_state

    sample_rate, audio_chunk = new_chunk_tuple
    if audio_chunk is None or sample_rate is None or audio_chunk.size == 0:
        return accumulated_transcript_state, current_summary_state, accumulated_transcript_state, last_summary_time_state, current_summary_state

    # Convert to float32 if needed
    if audio_chunk.dtype != np.float32:
        audio_chunk = audio_chunk.astype(np.float32) / 32768.0  

    # Speech-to-text processing
    try:
        result = stt_pipeline({"sampling_rate": sample_rate, "raw": audio_chunk.copy()})
        new_text = result["text"].strip() if result["text"] else ""
    except Exception as e:
        new_text = f"[Transcription Error: {e}]"

    updated_transcript = accumulated_transcript_state + " " + new_text if accumulated_transcript_state else new_text

    # Summarization every SUMMARY_INTERVAL
    current_time = time.time()
    new_summary = current_summary_state
    if updated_transcript and len(updated_transcript) > 50 and (current_time - last_summary_time_state > SUMMARY_INTERVAL):
        try:
            summary_result = summarizer(updated_transcript, max_length=150, min_length=30, do_sample=False)
            raw_summary = summary_result[0]['summary_text']
            new_summary = format_summary_as_bullets(raw_summary)
            last_summary_time_state = current_time  
        except Exception as e:
            return updated_transcript, f"[Summarization Error]\n{current_summary_state}", updated_transcript, last_summary_time_state, current_summary_state

    return updated_transcript, new_summary, updated_transcript, last_summary_time_state, new_summary

# --- Gradio UI ---
print("Creating Gradio interface...")
with gr.Blocks() as demo:
    gr.Markdown("# 🎤 AI-Powered Meeting Notes with Google Meet Integration")
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Audio(sources=["microphone"], streaming=True, label="🎙 Live Audio", type="numpy")
            gr.Image(sources=["webcam"], label="📷 Webcam", streaming=True)  

        with gr.Column(scale=2):
            transcription_output = gr.Textbox(label="📝 Full Transcription", lines=10, interactive=False)
            summary_output = gr.Textbox(label=f"🔹 Summary (Updates ~{SUMMARY_INTERVAL}s)", lines=6, interactive=False)

    # Google Meet Button (opens in new tab)
    google_meet_button = gr.Button("Start Google Meet")
    google_meet_button.click(fn=lambda: None, inputs=[], outputs=[], _js="() => window.open('https://meet.google.com/new', '_blank')")

    # Streaming Audio Processing
    gr.Audio(sources=["microphone"], streaming=True).stream(
        fn=process_audio_stream,
        inputs=[gr.Audio(sources=["microphone"], streaming=True), gr.State(""), gr.State(0.0), gr.State("")],
        outputs=[transcription_output, summary_output, gr.State(""), gr.State(0.0), gr.State("")]
    )

    # Clear button
    def clear_state():
        return "", "", 0.0, ""
    clear_button = gr.Button("Clear Transcript & Summary")
    clear_button.click(fn=lambda: ("", "", "", 0.0, ""), inputs=[], outputs=[transcription_output, summary_output, gr.State(""), gr.State(0.0), gr.State("")])

print("Launching Gradio app...")
demo.queue()
demo.launch(debug=True, share=True)