Update app.py

app.py

@@ -12,7 +12,8 @@ from waitress import serve
 from simple_salesforce import Salesforce
 import requests  # Import requests for exception handling
 
-app = Flask(__name__)
+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"
@@ -21,15 +22,14 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 config = AutoConfig.from_pretrained("openai/whisper-small")
 config.update({"timeout": 60})  # Set timeout to 60 seconds
 
-# Your function where you generate and save the audio
+# Function to generate audio prompts
 def generate_audio_prompt(text, filename):
     try:
         tts = gTTS(text)
         tts.save(os.path.join("static", filename))
-    except gtts.tts.gTTSError as e:
+    except Exception as e:
         print(f"Error: {e}")
-        print("Retrying after 5 seconds...")
-        time.sleep(5)  # Wait for 5 seconds before retrying
+        time.sleep(5)  # Wait before retrying
         generate_audio_prompt(text, filename)
 
 # Generate required voice prompts
@@ -43,19 +43,6 @@ prompts = {
 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:
@@ -63,32 +50,41 @@ def convert_to_wav(input_path, output_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)}")
 
 # 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
-    print(f"Detected nonsilent parts: {nonsilent_parts}")
-    return len(nonsilent_parts) == 0  # If no speech detected
+    nonsilent_parts = detect_nonsilent(audio, min_silence_len=500, silence_thresh=audio.dBFS-16)
+    return len(nonsilent_parts) == 0
 
 # 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)}")
 
-# Function to create Salesforce record
-# API endpoint to receive data from voice bot
+# ✅ HOME ROUTE (Loads `index.html`)
+@app.route("/", methods=["GET"])
+def index():
+    return render_template("index.html")
+
+# ✅ DASHBOARD ROUTE
+@app.route("/dashboard", methods=["GET"])
+def dashboard():
+    return render_template("dashboard.html")
+
+# ✅ MENU PAGE ROUTE
+@app.route("/menu_page", methods=["GET"])
+def menu_page():
+    return render_template("menu_page.html")
+
+# ✅ LOGIN API
 @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
-    
+    data = request.json
     name = data.get('name')
     email = data.get('email')
     phone_number = data.get('phone_number')
@@ -96,7 +92,6 @@ def login():
     if not name or not email or not phone_number:
         return jsonify({'error': 'Missing required fields'}), 400
 
-    # Create a record in Salesforce
     try:
         customer_login = sf.Customer_Login__c.create({
             'Name': name,
@@ -107,6 +102,7 @@ def login():
     except Exception as e:
         return jsonify({'error': f'Failed to create record in Salesforce: {str(e)}'}), 500
 
+# ✅ REGISTER API
 @app.route("/submit", methods=["POST"])
 def submit():
     data = request.json
@@ -118,30 +114,19 @@ def submit():
         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'):
-            return jsonify({'success': True})
-        else:
-            return jsonify({'error': 'Failed to create record'}), 500
-
+        return jsonify({'success': True}), 200
     except Exception as e:
         return jsonify({'error': str(e)}), 500
 
-
-@app.route("/")
-def index():
-    return render_template("index.html")
-
+# ✅ TRANSCRIBE AUDIO API
 @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"]
@@ -150,66 +135,55 @@ def transcribe():
     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
+
+        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()
-        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, in real case this should be handled via front-end
-        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
+        salesforce_response = sf.Customer_Login__c.create({
+            'Name': name,
+            'Email__c': email,
+            'Phone_Number__c': phone_number
+        })
 
-            # If creation was successful, return the details
-            return jsonify({"text": transcribed_text, "salesforce_record": salesforce_response})
+        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
+# ✅ MENU API
+@app.route("/menu", methods=["GET"])
+def get_menu():
+    try:
+        query = "SELECT Name, Price__c, Ingredients__c, Category__c FROM Menu_Item__c"
+        result = sf.query(query)
+
+        menu_items = []
+        for item in result["records"]:
+            menu_items.append({
+                "name": item["Name"],
+                "price": item["Price__c"],
+                "ingredients": item["Ingredients__c"],
+                "category": item["Category__c"]
+            })
+
+        return jsonify({"success": True, "menu": menu_items})
+    except Exception as e:
+        return jsonify({"error": f"Failed to fetch menu: {str(e)}"}), 500
+
+# ✅ START PRODUCTION SERVER
 if __name__ == "__main__":
-    serve(app, host="0.0.0.0", port=7860)
+    print("✅ Starting Flask API Server on port 7860...")
+    serve(app, host="0.0.0.0", port=7860)