voicemenu1433 / app.py
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import torch
from flask import Flask, render_template, request, jsonify, redirect
import json
import os
from transformers import pipeline
from gtts import gTTS
from pydub import AudioSegment
from pydub.silence import detect_nonsilent
from transformers import AutoConfig # Import AutoConfig for the config object
import time
from waitress import serve
from simple_salesforce import Salesforce
import requests # Import requests for exception handling
app = Flask(__name__)
# Use whisper-small for faster processing and better speed
device = "cuda" if torch.cuda.is_available() else "cpu"
# Create config object to set timeout and other parameters
config = AutoConfig.from_pretrained("openai/whisper-small")
config.update({"timeout": 60}) # Set timeout to 60 seconds
# Salesforce connection details
try:
print("Attempting to connect to Salesforce...")
sf = Salesforce(username='[email protected]', password='Sati@1020', security_token='sSSjyhInIsUohKpG8sHzty2q')
print("Connected to Salesforce successfully!")
print("User Info:", sf.UserInfo) # Log the user info to verify the connection
except Exception as e:
print(f"Failed to connect to Salesforce: {str(e)}")
# Functions for Salesforce operations
def create_salesforce_record(sf, name, email, phone_number):
try:
customer_login = sf.Customer_Login__c.create({
'Name': name,
'Email__c': email,
'Phone_Number__c': phone_number
})
return customer_login
except Exception as e:
raise Exception(f"Failed to create record: {str(e)}")
def get_menu_items(sf):
query = "SELECT Name, Price__c, Ingredients__c, Category__c FROM Menu_Item__c"
result = sf.query(query)
return result['records']
# Voice-related functions
def generate_audio_prompt(text, filename):
try:
tts = gTTS(text)
tts.save(os.path.join("static", filename))
except gtts.tts.gTTSError as e:
print(f"Error: {e}")
print("Retrying after 5 seconds...")
time.sleep(5) # Wait for 5 seconds before retrying
generate_audio_prompt(text, filename)
# Utility functions
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:
print(f"Error: {str(e)}")
raise Exception(f"Audio conversion failed: {str(e)}")
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
print(f"Detected nonsilent parts: {nonsilent_parts}")
return len(nonsilent_parts) == 0 # If no speech detected
# Routes and Views
@app.route("/")
def index():
return render_template("index.html")
@app.route("/dashboard", methods=["GET"])
def dashboard():
return render_template("dashboard.html") # Render the dashboard template
@app.route('/login', methods=['POST'])
def login():
# Get data from voice bot (name, email, phone number)
data = request.json # Assuming voice bot sends JSON data
name = data.get('name')
email = data.get('email')
phone_number = data.get('phone_number')
if not name or not email or not phone_number:
return jsonify({'error': 'Missing required fields'}), 400
try:
customer_login = create_salesforce_record(sf, name, email, phone_number)
return redirect("/menu") # Redirect to the menu page after successful login
except Exception as e:
return jsonify({'error': f'Failed to create record in Salesforce: {str(e)}'}), 500
@app.route("/submit", methods=["POST"])
def submit():
data = request.json
name = data.get('name')
email = data.get('email')
phone = data.get('phone')
if not name or not email or not phone:
return jsonify({'error': 'Missing data'}), 400
try:
# Create Salesforce record
customer_login = sf.Customer_Login__c.create({
'Name': name,
'Email__c': email,
'Phone_Number__c': phone
})
if customer_login.get('id'):
print(f"Success: Customer record created with ID: {customer_login['id']}")
return jsonify({'success': True})
else:
print("Failed: No ID returned after creating the record.")
return jsonify({'error': 'Failed to create record'}), 500
except Exception as e:
print(f"Error: {str(e)}") # Print error message if an exception occurs
return jsonify({'error': str(e)}), 500
@app.route("/menu", methods=["GET"])
def menu_page():
menu_items = get_menu_items(sf) # Fetch menu items from Salesforce
menu_data = [{"name": item['Name'], "price": item['Price__c'], "ingredients": item['Ingredients__c'], "category": item['Category__c']} for item in menu_items]
return render_template("menu_page.html", menu_items=menu_data)
# Route for handling order
@app.route("/order", methods=["POST"])
def place_order():
item_name = request.json.get('item_name')
quantity = request.json.get('quantity')
order_data = {"Item__c": item_name, "Quantity__c": quantity}
sf.Order__c.create(order_data)
return jsonify({"success": True, "message": f"Order for {item_name} placed successfully."})
# Route to handle the cart
@app.route("/cart", methods=["GET"])
def cart():
cart_items = [] # Placeholder for cart items
return render_template("cart_page.html", cart_items=cart_items)
# Route for the order summary page
@app.route("/order-summary", methods=["GET"])
def order_summary():
order_details = [] # Placeholder for order details
return render_template("order_summary.html", order_details=order_details)
@app.route("/transcribe", methods=["POST"])
def transcribe():
if "audio" not in request.files:
print("No audio file provided")
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
else:
print("Audio contains speech, proceeding with transcription.")
# Use Whisper ASR model for transcription
result = None
retry_attempts = 3
for attempt in range(retry_attempts):
try:
result = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=0 if torch.cuda.is_available() else -1, config=config)
print(f"Transcribed text: {result['text']}")
break
except requests.exceptions.ReadTimeout:
print(f"Timeout occurred, retrying attempt {attempt + 1}/{retry_attempts}...")
time.sleep(5)
if result is None:
return jsonify({"error": "Unable to transcribe audio after retries."}), 500
transcribed_text = result["text"].strip().capitalize()
print(f"Transcribed text: {transcribed_text}")
# Extract name, email, and phone number from the transcribed text
parts = transcribed_text.split()
name = parts[0] if len(parts) > 0 else "Unknown Name"
email = parts[1] if '@' in parts[1] else "[email protected]"
phone_number = parts[2] if len(parts) > 2 else "0000000000"
print(f"Parsed data - Name: {name}, Email: {email}, Phone Number: {phone_number}")
# Confirm details before submission
confirmation = f"Is this correct? Name: {name}, Email: {email}, Phone: {phone_number}"
generate_audio_prompt(confirmation, "confirmation.mp3")
# Simulate confirmation via user action
user_confirms = True # Assuming the user confirms, you can replace this with actual user input logic
if user_confirms:
# Create record in Salesforce
salesforce_response = create_salesforce_record(name, email, phone_number)
# Log the Salesforce response
print(f"Salesforce record creation response: {salesforce_response}")
# Check if the response contains an error
if "error" in salesforce_response:
print(f"Error creating record in Salesforce: {salesforce_response['error']}")
return jsonify(salesforce_response), 500
return jsonify({"text": transcribed_text, "salesforce_record": salesforce_response})
except Exception as e:
print(f"Error in transcribing or processing: {str(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)