import torch
from flask import Flask, render_template, request, jsonify
import os
from transformers import pipeline
from gtts import gTTS
from pydub import AudioSegment
from pydub.silence import detect_nonsilent
from waitress import serve
from flask import Flask, render_template, request, jsonify, redirect, url_for, session
from flask_session import Session  # Import the Session class
from flask.sessions import SecureCookieSessionInterface  # Import the class
from salesforce import get_salesforce_connection
import os
import logging

logging.basicConfig(level=logging.INFO)
logging.info("This is an info message")
logging.error("This is an error message")




# Initialize Flask app and Salesforce connection
print("Starting app...")
app = Flask(__name__)
print("Flask app initialized.")

# Add debug logs in Salesforce connection setup
sf = get_salesforce_connection()
print("Salesforce connection established.")

# Set the secret key to handle sessions securely
app.secret_key = os.getenv("SECRET_KEY", "sSSjyhInIsUohKpG8sHzty2q")  # Replace with a secure key

# Configure the session type
app.config["SESSION_TYPE"] = "filesystem"  # Use filesystem for session storage
#app.config["SESSION_COOKIE_NAME"] = "my_session"  # Optional: Change session cookie name
app.config["SESSION_COOKIE_SECURE"] = True  # Ensure cookies are sent over HTTPS
app.config["SESSION_COOKIE_SAMESITE"] = "None"  # Allow cross-site cookies

# Initialize the session
Session(app)  # Correctly initialize the Session object
print("Session interface configured.")

# Ensure secure session handling for environments like Hugging Face
app.session_interface = SecureCookieSessionInterface()
print("Session interface configured.")

app = Flask(__name__)

# Use whisper-small for faster processing and better speed
device = "cuda" if torch.cuda.is_available() else "cpu"
asr_model = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=0 if device == "cuda" else -1)

def query_salesforce_data(sf):
    # Example of querying a customer record
    try:
        # Query the Customer_Login__c object
        customers = sf.query("SELECT Id, Name FROM Customer_Login__c LIMIT 5")
        
        # Loop through the records returned by the query
        for customer in customers['records']:
            print(customer['Id'], customer['Name'])
    except Exception as e:
        print(f"Error querying Salesforce: {e}")
        
result = query_salesforce_data(sf)

# Function to generate audio prompts
def generate_audio_prompt(text, filename):
    tts = gTTS(text=text, lang="en")
    tts.save(os.path.join("static", filename))

# Generate required voice prompts
prompts = {
    "welcome": "Welcome to Biryani Hub.",
    "ask_name": "Tell me your name.",
    "ask_email": "Please provide your email address.",
    "thank_you": "Thank you for registration."
}

for key, text in prompts.items():
    generate_audio_prompt(text, f"{key}.mp3")

# Symbol mapping for proper recognition
SYMBOL_MAPPING = {
    "at the rate": "@",
    "at": "@",
    "dot": ".",
    "underscore": "_",
    "hash": "#",
    "plus": "+",
    "dash": "-",
    "comma": ",",
    "space": " "
}

# Function to convert audio to WAV format
def convert_to_wav(input_path, output_path):
    try:
        audio = AudioSegment.from_file(input_path)
        audio = audio.set_frame_rate(16000).set_channels(1)  # Convert to 16kHz, mono
        audio.export(output_path, format="wav")
    except Exception as e:
        raise Exception(f"Audio conversion failed: {str(e)}")

# Function to check if audio contains actual speech
def is_silent_audio(audio_path):
    audio = AudioSegment.from_wav(audio_path)
    nonsilent_parts = detect_nonsilent(audio, min_silence_len=500, silence_thresh=audio.dBFS-16)  # Reduced silence duration
    return len(nonsilent_parts) == 0  # If no speech detected

@app.route("/")
def index():
    return render_template("index.html")

@app.route("/transcribe", methods=["POST"])
def transcribe():
    if "audio" not in request.files:
        return jsonify({"error": "No audio file provided"}), 400

    audio_file = request.files["audio"]
    input_audio_path = os.path.join("static", "temp_input.wav")
    output_audio_path = os.path.join("static", "temp.wav")
    audio_file.save(input_audio_path)

    try:
        # Convert to WAV
        convert_to_wav(input_audio_path, output_audio_path)

        # Check for silence
        if is_silent_audio(output_audio_path):
            return jsonify({"error": "No speech detected. Please try again."}), 400

        # Use Whisper ASR model for transcription
        result = asr_model(output_audio_path, generate_kwargs={"language": "en"})
        transcribed_text = result["text"].strip().capitalize()

        return jsonify({"text": transcribed_text})
    except Exception as e:
        return jsonify({"error": f"Speech recognition error: {str(e)}"}), 500

# Start Production Server
if __name__ == "__main__":
    serve(app, host="0.0.0.0", port=7860)