import gradio as gr
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import numpy as np
from pyannote.audio import Pipeline
import os
from dotenv import load_dotenv
import plotly.graph_objects as go

load_dotenv()

# Check and set device
device = "cuda:0" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Model and pipeline setup
model_id = "distil-whisper/distil-small.en"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)

processor = AutoProcessor.from_pretrained(model_id)

pipe = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    torch_dtype=torch_dtype,
    device=device,
)

# diarization pipeline (renamed to avoid conflict)
diarization_pipeline = Pipeline.from_pretrained(
    "pyannote/speaker-diarization-3.0", use_auth_token=os.getenv("HF_KEY")
)


def diarization_info(res):
    starts = []
    ends = []
    speakers = []

    for segment, track, _ in res.itertracks(yield_label=True):
        starts.append(segment.start)
        ends.append(segment.end)
        speakers.append(track)

    return starts, ends, speakers


def plot_diarization(starts, ends, speakers):
    fig = go.Figure()

    # Define a color map for different speakers
    num_speakers = len(set(speakers))
    colors = [f"hsl({h},80%,60%)" for h in np.linspace(0, 360, num_speakers)]

    # Plot each segment with its speaker's color
    for start, end, speaker in zip(starts, ends, speakers):
        speaker_id = list(set(speakers)).index(speaker)
        fig.add_trace(
            go.Scatter(
                x=[start, end],
                y=[speaker_id, speaker_id],
                mode="lines",
                line=dict(color=colors[speaker_id], width=15),
                showlegend=False,
            )
        )

    fig.update_layout(
        title="Speaker Diarization",
        xaxis=dict(title="Time"),
        yaxis=dict(title="Speaker"),
        height=600,
        width=800,
    )

    return fig


def transcribe_diarize(audio):
    sr, data = audio
    processed_data = np.array(data).astype(np.float32) / 32767.0
    waveform_tensor = torch.tensor(processed_data[np.newaxis, :])

    # results from the pipeline
    transcription_res = pipe({"sampling_rate": sr, "raw": processed_data})["text"]
    diarization_res = diarization_pipeline(
        {"waveform": waveform_tensor, "sample_rate": sr}
    )

    # Get diarization information
    starts, ends, speakers = diarization_info(diarization_res)

    # Plot diarization
    diarization_plot = plot_diarization(starts, ends, speakers)

    return transcription_res, diarization_res, diarization_plot


# creating the gradio interface
demo = gr.Interface(
    fn=transcribe_diarize,
    inputs=gr.Audio(sources=["upload", "microphone"]),
    outputs=[
        gr.Textbox(lines=3, label="Text Transcription"),
        gr.Textbox(label="Speaker Diarization"),
        gr.Plot(),
    ],
    title="Automatic Speech Recognition with Diarization 🗣️",
    description="Transcribe your speech to text with distilled whisper and diarization with pyannote. Get started by recording from your mic or uploading an audio file 🎙️",
)


if __name__ == "__main__":
    demo.launch()