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Update app.py
Browse files
app.py
CHANGED
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import torch
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from flask import Flask, render_template, request, jsonify
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import json
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import os
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from transformers import pipeline
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from gtts import gTTS
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from pydub import AudioSegment
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from pydub.silence import detect_nonsilent
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from transformers import AutoConfig
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import time
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from waitress import serve
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from simple_salesforce import Salesforce
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import requests
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app = Flask(__name__, template_folder="templates")
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app.secret_key = os.urandom(24)
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#
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#
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try:
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print("Attempting to connect to Salesforce...")
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sf = Salesforce(username='[email protected]',
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print("✅ Connected to Salesforce successfully!")
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except Exception as e:
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print(f"
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#
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#
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# ✅ RENDER MENU PAGE
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@app.route("/menu_page", methods=["GET"])
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def menu_page():
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return render_template("menu_page.html")
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def register():
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print("➡ Register API hit")
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data = request.json
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if not data or "name" not in data or "email" not in data or "phone" not in data:
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return jsonify({"error": "Missing required fields"}), 400
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try:
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'Name':
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'Email__c':
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'Phone_Number__c':
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})
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return jsonify({
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except Exception as e:
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return jsonify({
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def login():
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print("➡ Login API hit")
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data = request.json
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try:
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"customer_id": user["Id"],
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"name": user["Name"]
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})
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else:
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return jsonify({
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except Exception as e:
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return jsonify({
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# ✅ MENU API: Fetch Menu Items from Salesforce
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@app.route("/menu", methods=["GET"])
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def get_menu():
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print("➡ Menu API hit")
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try:
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except Exception as e:
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#
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if __name__ == "__main__":
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serve(app, host="0.0.0.0", port=7860)
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import torch
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from flask import Flask, render_template, request, jsonify
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import json
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import os
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from transformers import pipeline
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from gtts import gTTS
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from pydub import AudioSegment
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from pydub.silence import detect_nonsilent
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from transformers import AutoConfig # Import AutoConfig for the config object
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import time
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from waitress import serve
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from simple_salesforce import Salesforce
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import requests # Import requests for exception handling
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app = Flask(__name__)
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# Use whisper-small for faster processing and better speed
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# Create config object to set timeout and other parameters
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config = AutoConfig.from_pretrained("openai/whisper-small")
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config.update({"timeout": 60}) # Set timeout to 60 seconds
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# Your function where you generate and save the audio
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def generate_audio_prompt(text, filename):
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try:
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tts = gTTS(text)
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tts.save(os.path.join("static", filename))
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except gtts.tts.gTTSError as e:
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print(f"Error: {e}")
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print("Retrying after 5 seconds...")
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time.sleep(5) # Wait for 5 seconds before retrying
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generate_audio_prompt(text, filename)
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# Generate required voice prompts
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prompts = {
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"welcome": "Welcome to Biryani Hub.",
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"ask_name": "Tell me your name.",
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"ask_email": "Please provide your email address.",
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"thank_you": "Thank you for registration."
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}
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for key, text in prompts.items():
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generate_audio_prompt(text, f"{key}.mp3")
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# Symbol mapping for proper recognition
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SYMBOL_MAPPING = {
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"at the rate": "@",
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"at": "@",
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"dot": ".",
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"underscore": "_",
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"hash": "#",
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"plus": "+",
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"dash": "-",
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"comma": ",",
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"space": " "
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}
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# Function to convert audio to WAV format
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def convert_to_wav(input_path, output_path):
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try:
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audio = AudioSegment.from_file(input_path)
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audio = audio.set_frame_rate(16000).set_channels(1) # Convert to 16kHz, mono
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audio.export(output_path, format="wav")
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except Exception as e:
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print(f"Error: {str(e)}")
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raise Exception(f"Audio conversion failed: {str(e)}")
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# Function to check if audio contains actual speech
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def is_silent_audio(audio_path):
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audio = AudioSegment.from_wav(audio_path)
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nonsilent_parts = detect_nonsilent(audio, min_silence_len=500, silence_thresh=audio.dBFS-16) # Reduced silence duration
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print(f"Detected nonsilent parts: {nonsilent_parts}")
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return len(nonsilent_parts) == 0 # If no speech detected
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# Salesforce connection details
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try:
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print("Attempting to connect to Salesforce...")
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sf = Salesforce(username='[email protected]', password='Sati@1020', security_token='sSSjyhInIsUohKpG8sHzty2q')
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print("Connected to Salesforce successfully!")
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print("User Info:", sf.UserInfo) # Log the user info to verify the connection
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except Exception as e:
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print(f"Failed to connect to Salesforce: {str(e)}")
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# Function to create Salesforce record
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# API endpoint to receive data from voice bot
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@app.route('/login', methods=['POST'])
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def login():
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# Get data from voice bot (name, email, phone number)
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data = request.json # Assuming voice bot sends JSON data
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name = data.get('name')
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email = data.get('email')
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phone_number = data.get('phone_number')
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if not name or not email or not phone_number:
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return jsonify({'error': 'Missing required fields'}), 400
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# Create a record in Salesforce
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try:
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customer_login = sf.Customer_Login__c.create({
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'Name': name,
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'Email__c': email,
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'Phone_Number__c': phone_number
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})
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return jsonify({'success': True, 'id': customer_login['id']}), 200
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except Exception as e:
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return jsonify({'error': f'Failed to create record in Salesforce: {str(e)}'}), 500
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@app.route("/submit", methods=["POST"])
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def submit():
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data = request.json
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name = data.get('name')
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email = data.get('email')
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phone = data.get('phone')
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if not name or not email or not phone:
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return jsonify({'error': 'Missing data'}), 400
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try:
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# Create Salesforce record
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customer_login = sf.Customer_Login__c.create({
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'Name': name,
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'Email__c': email,
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'Phone_Number__c': phone
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})
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if customer_login.get('id'):
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return jsonify({'success': True})
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else:
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return jsonify({'error': 'Failed to create record'}), 500
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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@app.route("/")
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def index():
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return render_template("index.html")
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@app.route("/transcribe", methods=["POST"])
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def transcribe():
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if "audio" not in request.files:
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print("No audio file provided")
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return jsonify({"error": "No audio file provided"}), 400
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audio_file = request.files["audio"]
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input_audio_path = os.path.join("static", "temp_input.wav")
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output_audio_path = os.path.join("static", "temp.wav")
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audio_file.save(input_audio_path)
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try:
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# Convert to WAV
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convert_to_wav(input_audio_path, output_audio_path)
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# Check for silence
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if is_silent_audio(output_audio_path):
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return jsonify({"error": "No speech detected. Please try again."}), 400
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else:
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print("Audio contains speech, proceeding with transcription.")
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# Use Whisper ASR model for transcription
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result = None
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retry_attempts = 3
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for attempt in range(retry_attempts):
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try:
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result = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=0 if torch.cuda.is_available() else -1, config=config)
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print(f"Transcribed text: {result['text']}")
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break
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except requests.exceptions.ReadTimeout:
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print(f"Timeout occurred, retrying attempt {attempt + 1}/{retry_attempts}...")
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time.sleep(5)
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if result is None:
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return jsonify({"error": "Unable to transcribe audio after retries."}), 500
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transcribed_text = result["text"].strip().capitalize()
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print(f"Transcribed text: {transcribed_text}")
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# Extract name, email, and phone number from the transcribed text
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parts = transcribed_text.split()
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name = parts[0] if len(parts) > 0 else "Unknown Name"
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email = parts[1] if '@' in parts[1] else "[email protected]"
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phone_number = parts[2] if len(parts) > 2 else "0000000000"
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print(f"Parsed data - Name: {name}, Email: {email}, Phone Number: {phone_number}")
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# Confirm details before submission
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confirmation = f"Is this correct? Name: {name}, Email: {email}, Phone: {phone_number}"
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generate_audio_prompt(confirmation, "confirmation.mp3")
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# Simulate confirmation via user action, in real case this should be handled via front-end
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user_confirms = True # Assuming the user confirms, you can replace this with actual user input logic
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if user_confirms:
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# Create record in Salesforce
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salesforce_response = create_salesforce_record(name, email, phone_number)
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# Log the Salesforce response
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print(f"Salesforce record creation response: {salesforce_response}")
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# Check if the response contains an error
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if "error" in salesforce_response:
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print(f"Error creating record in Salesforce: {salesforce_response['error']}")
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return jsonify(salesforce_response), 500
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# If creation was successful, return the details
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return jsonify({"text": transcribed_text, "salesforce_record": salesforce_response})
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except Exception as e:
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print(f"Error in transcribing or processing: {str(e)}")
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return jsonify({"error": f"Speech recognition error: {str(e)}"}), 500
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# Start Production Server
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if __name__ == "__main__":
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serve(app, host="0.0.0.0", port=7860)
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