Update app.py

app.py

@@ -6,65 +6,22 @@ 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
+from transformers import AutoConfig
 import time
 from waitress import serve
 from simple_salesforce import Salesforce
-import requests  # Import requests for exception handling
+import requests
 
 app = Flask(__name__, template_folder="templates")
 app.secret_key = os.urandom(24)
 
-# 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
-
-# Function to generate audio prompts
-def generate_audio_prompt(text, filename):
-    try:
-        tts = gTTS(text)
-        tts.save(os.path.join("static", filename))
-    except Exception as e:
-        print(f"Error: {e}")
-        time.sleep(5)  # Wait before retrying
-        generate_audio_prompt(text, 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")
-
-# 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)
-    return len(nonsilent_parts) == 0
-
-# Salesforce connection details
+# ✅ Salesforce Connection Setup
 try:
     print("Attempting to connect to Salesforce...")
     sf = Salesforce(username='[email protected]', password='Sati@1020', security_token='sSSjyhInIsUohKpG8sHzty2q')
-    print("Connected to Salesforce successfully!")
+    print("✅ Connected to Salesforce successfully!")
 except Exception as e:
-    print(f"Failed to connect to Salesforce: {str(e)}")
+    print(f"❌ Failed to connect to Salesforce: {str(e)}")
 
 # ✅ HOME ROUTE (Loads `index.html`)
 @app.route("/", methods=["GET"])
@@ -76,7 +33,7 @@ def index():
 def dashboard():
     return render_template("dashboard.html")
 
-# ✅ MENU PAGE ROUTE
+# ✅ MENU PAGE ROUTE (NEWLY ADDED)
 @app.route("/menu_page", methods=["GET"])
 def menu_page():
     return render_template("menu_page.html")
@@ -123,46 +80,6 @@ def submit():
     except Exception as e:
         return jsonify({'error': str(e)}), 500
 
-# ✅ TRANSCRIBE AUDIO API
-@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(input_audio_path, output_audio_path)
-        if is_silent_audio(output_audio_path):
-            return jsonify({"error": "No speech detected. Please try again."}), 400
-
-        asr_pipeline = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=0 if torch.cuda.is_available() else -1, config=config)
-        result = asr_pipeline(output_audio_path)
-
-        transcribed_text = result["text"].strip().capitalize()
-
-        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"
-
-        confirmation = f"Is this correct? Name: {name}, Email: {email}, Phone: {phone_number}"
-        generate_audio_prompt(confirmation, "confirmation.mp3")
-
-        salesforce_response = sf.Customer_Login__c.create({
-            'Name': name,
-            'Email__c': email,
-            'Phone_Number__c': phone_number
-        })
-
-        return jsonify({"text": transcribed_text, "salesforce_record": salesforce_response})
-
-    except Exception as e:
-        return jsonify({"error": f"Speech recognition error: {str(e)}"}), 500
-
 # ✅ MENU API
 @app.route("/menu", methods=["GET"])
 def get_menu():