# Salience API
# ============
# Uses a worker thread for model inference to avoid fork() issues with Metal/MPS.
# The worker thread owns all model instances; HTTP handlers submit work via queue.

print("Starting salience __init__.py...")

from flask import Flask, request
from flask_cors import CORS
import numpy as np
from .salience import submit_work, AVAILABLE_MODELS
import json
import time
from collections import deque
import threading

app = Flask(__name__)
CORS(app, origins=["http://localhost:5173"])

# Thread-safe stats tracker for this worker process
class StatsTracker:
    def __init__(self):
        # Store (start_time, end_time, duration) for successful requests
        self.processing_spans = deque(maxlen=1000)
        # Store arrival timestamps for overflow requests
        self.overflow_arrivals = deque(maxlen=1000)
        self.lock = threading.Lock()

    def add_processing_span(self, start_time, end_time):
        duration = end_time - start_time
        with self.lock:
            self.processing_spans.append((start_time, end_time, duration))
            # Clean old entries (>5 min)
            cutoff = time.time() - 300
            while self.processing_spans and self.processing_spans[0][0] < cutoff:
                self.processing_spans.popleft()

    def add_overflow_arrival(self, arrival_time):
        with self.lock:
            self.overflow_arrivals.append(arrival_time)
            # Clean old entries (>5 min)
            cutoff = time.time() - 300
            while self.overflow_arrivals and self.overflow_arrivals[0] < cutoff:
                self.overflow_arrivals.popleft()

    def get_stats(self):
        with self.lock:
            return {
                'processing_spans': [
                    {'start': start, 'end': end, 'duration': duration}
                    for start, end, duration in self.processing_spans
                ],
                'overflow_arrivals': list(self.overflow_arrivals),
                'window_seconds': 300  # 5 minutes
            }

stats_tracker = StatsTracker()

# Load default text from transcript.txt for GET requests
with open('./transcript.txt', 'r') as file:
    default_source_text = file.read().strip()

@app.route("/models")
def models_view():
    return json.dumps(list(AVAILABLE_MODELS.keys()))

@app.route("/overflow", methods=['GET', 'POST'])
def overflow_view():
    """
    Endpoint hit when HAProxy queue is full.
    Returns 429 with statistics about processing and overflow.
    """
    arrival_time = time.time()
    stats_tracker.add_overflow_arrival(arrival_time)

    stats = stats_tracker.get_stats()

    response = {
        'error': 'Queue full',
        'status': 429,
        'stats': stats,
        'message': 'Service is at capacity. Try again or check queue statistics.'
    }

    return json.dumps(response), 429

@app.route("/stats")
def stats_view():
    """
    Endpoint for frontend to poll current queue statistics.
    Returns processing spans and overflow arrivals from last 5 minutes.
    """
    stats = stats_tracker.get_stats()
    return json.dumps(stats)

@app.route("/salience", methods=['GET'])
def salience_view_default():
    """GET endpoint - processes default text from transcript.txt"""
    start_time = time.time()

    model_name = request.args.get('model', 'all-mpnet-base-v2')

    # Validate model name
    if model_name not in AVAILABLE_MODELS:
        return json.dumps({'error': f'Invalid model: {model_name}'}), 400

    sentence_ranges, adjacency = submit_work(default_source_text, model_name)

    end_time = time.time()
    stats_tracker.add_processing_span(start_time, end_time)

    return json.dumps({
        'source': default_source_text,
        'intervals': sentence_ranges,
        'adjacency': np.nan_to_num(adjacency.numpy()).tolist(),
        'model': model_name,
    })

@app.route("/salience", methods=['POST'])
def salience_view_custom():
    """POST endpoint - processes text from request body"""
    start_time = time.time()

    model_name = request.args.get('model', 'all-mpnet-base-v2')

    # Validate model name
    if model_name not in AVAILABLE_MODELS:
        return json.dumps({'error': f'Invalid model: {model_name}'}), 400

    # Get document content from request body as plain text
    source_text = request.data.decode('utf-8').strip()

    if not source_text:
        return json.dumps({'error': 'No text provided'}), 400

    sentence_ranges, adjacency = submit_work(source_text, model_name)

    end_time = time.time()
    stats_tracker.add_processing_span(start_time, end_time)

    return json.dumps({
        'source': source_text,
        'intervals': sentence_ranges,
        'adjacency': np.nan_to_num(adjacency.numpy()).tolist(),
        'model': model_name,
    })