Merge branch 'main' of https://github.com/GamerBoss101/HooHacks-12

Backend/server.py

@@ -8,7 +8,6 @@ import logging
 import numpy as np
 import torch
 import torchaudio
-import whisperx
 from io import BytesIO
 from typing import List, Dict, Any, Optional
 from flask import Flask, request, send_from_directory, Response
@@ -17,6 +16,8 @@ from flask_socketio import SocketIO, emit, disconnect
 from generator import load_csm_1b, Segment
 from collections import deque
 from threading import Lock
+from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
 # Configure logging
 logging.basicConfig(
@@ -25,68 +26,24 @@ logging.basicConfig(
 )
 logger = logging.getLogger("sesame-server")
 
-# CUDA Environment Setup
+# Determine best compute device
-def setup_cuda_environment():
+if torch.backends.mps.is_available():
-    """Set up CUDA environment with proper error handling"""
+    device = "mps"
-    # Search for CUDA libraries in common locations
+elif torch.cuda.is_available():
-    cuda_lib_dirs = [
-        "/usr/local/cuda/lib64",
-        "/usr/lib/x86_64-linux-gnu",
-        "/usr/local/cuda/extras/CUPTI/lib64"
-    ]
-    
-    # Add directories to LD_LIBRARY_PATH if they exist
-    current_ld_path = os.environ.get('LD_LIBRARY_PATH', '')
-    for cuda_dir in cuda_lib_dirs:
-        if os.path.exists(cuda_dir) and cuda_dir not in current_ld_path:
-            if current_ld_path:
-                os.environ['LD_LIBRARY_PATH'] = f"{current_ld_path}:{cuda_dir}"
-            else:
-                os.environ['LD_LIBRARY_PATH'] = cuda_dir
-            current_ld_path = os.environ['LD_LIBRARY_PATH']
-    
-    logger.info(f"LD_LIBRARY_PATH set to: {os.environ.get('LD_LIBRARY_PATH', 'not set')}")
-    
-    # Determine best compute device
-    device = "cpu"
-    compute_type = "int8"
-    
-    try:
-        # Set CUDA preferences
-        os.environ["CUDA_VISIBLE_DEVICES"] = "0"  # Limit to first GPU only
-        
-        # Try enabling TF32 precision if available
     try:
+        # Test CUDA functionality
+        torch.rand(10, device="cuda")
         torch.backends.cuda.matmul.allow_tf32 = True
         torch.backends.cudnn.allow_tf32 = True
-            torch.backends.cudnn.enabled = True
         torch.backends.cudnn.benchmark = True
-        except Exception as e:
-            logger.warning(f"Could not set advanced CUDA options: {e}")
-        
-        # Test if CUDA is functional
-        if torch.cuda.is_available():
-            try:
-                # Test basic CUDA operations
-                x = torch.rand(10, device="cuda")
-                y = x + x
-                del x, y
-                torch.cuda.empty_cache()
         device = "cuda"
-                compute_type = "float16"
         logger.info("CUDA is fully functional")
     except Exception as e:
         logger.warning(f"CUDA available but not working correctly: {e}")
         device = "cpu"
-        else:
+else:
-            logger.info("CUDA is not available, using CPU")
+    device = "cpu"
-    except Exception as e:
+    logger.info("Using CPU")
-        logger.error(f"Error setting up computing environment: {e}")
-    
-    return device, compute_type
-
-# Set up the compute environment
-device, compute_type = setup_cuda_environment()
 
 # Constants and Configuration
 SILENCE_THRESHOLD = 0.01
@@ -99,73 +56,106 @@ base_dir = os.path.dirname(os.path.abspath(__file__))
 static_dir = os.path.join(base_dir, "static")
 os.makedirs(static_dir, exist_ok=True)
 
+# Define a simple energy-based speech detector
+class SpeechDetector:
+    def __init__(self):
+        self.min_speech_energy = 0.01
+        self.speech_window = 0.2  # seconds
+    
+    def detect_speech(self, audio_tensor, sample_rate):
+        # Calculate frame size based on window size
+        frame_size = int(sample_rate * self.speech_window)
+        
+        # If audio is shorter than frame size, use the entire audio
+        if audio_tensor.shape[0] < frame_size:
+            frames = [audio_tensor]
+        else:
+            # Split audio into frames
+            frames = [audio_tensor[i:i+frame_size] for i in range(0, len(audio_tensor), frame_size)]
+        
+        # Calculate energy per frame
+        energies = [torch.mean(frame**2).item() for frame in frames]
+        
+        # Determine if there's speech based on energy threshold
+        has_speech = any(e > self.min_speech_energy for e in energies)
+        
+        return has_speech
+
+speech_detector = SpeechDetector()
+logger.info("Initialized simple speech detector")
+
 # Model Loading Functions
 def load_speech_models():
-    """Load all required speech models with fallbacks"""
+    """Load speech generation and recognition models"""
-    # Load speech generation model (Sesame CSM)
+    # Load CSM (existing code)
-    try:
-        logger.info(f"Loading Sesame CSM model on {device}...")
     generator = load_csm_1b(device=device)
-        logger.info("Sesame CSM model loaded successfully")
+    
-    except Exception as e:
+    # Load Whisper model for speech recognition
-        logger.error(f"Error loading Sesame CSM on {device}: {e}")
-        if device == "cuda":
     try:
-                logger.info("Trying to load Sesame CSM on CPU instead...")
+        logger.info(f"Loading speech recognition model on {device}...")
-                generator = load_csm_1b(device="cpu")
+        speech_recognizer = pipeline("automatic-speech-recognition", 
-                logger.info("Sesame CSM model loaded on CPU successfully")
+                                    model="openai/whisper-small", 
-            except Exception as cpu_error:
+                                    device=device)
-                logger.critical(f"Failed to load speech synthesis model: {cpu_error}")
+        logger.info("Speech recognition model loaded successfully")
-                raise RuntimeError("Failed to load speech synthesis model")
-        else:
-            raise RuntimeError("Failed to load speech synthesis model on any device")
-    
-    # Load ASR model (WhisperX)
-    try:
-        logger.info("Loading WhisperX model...")
-        # Start with the tiny model on CPU for reliable initialization
-        asr_model = whisperx.load_model("tiny", "cpu", compute_type="int8")
-        logger.info("WhisperX 'tiny' model loaded on CPU successfully")
-        
-        # Try upgrading to GPU if available
-        if device == "cuda":
-            try:
-                logger.info("Trying to load WhisperX on CUDA...")
-                # Test with a tiny model first
-                test_audio = torch.zeros(16000)  # 1 second of silence
-                
-                cuda_model = whisperx.load_model("tiny", "cuda", compute_type="float16")
-                # Test the model with real inference
-                _ = cuda_model.transcribe(test_audio.numpy(), batch_size=1)
-                asr_model = cuda_model
-                logger.info("WhisperX model running on CUDA successfully")
-                
-                # Try to upgrade to small model
-                try:
-                    small_model = whisperx.load_model("small", "cuda", compute_type="float16")
-                    _ = small_model.transcribe(test_audio.numpy(), batch_size=1)
-                    asr_model = small_model
-                    logger.info("WhisperX 'small' model loaded on CUDA successfully")
     except Exception as e:
-                    logger.warning(f"Staying with 'tiny' model on CUDA: {e}")
+        logger.error(f"Error loading speech recognition model: {e}")
+        speech_recognizer = None
+    
+    return generator, speech_recognizer
+
+# Unpack both models
+generator, speech_recognizer = load_speech_models()
+
+# Initialize Llama 3.2 model for conversation responses
+def load_llm_model():
+    """Load Llama 3.2 model for generating text responses"""
+    try:
+        logger.info("Loading Llama 3.2 model for conversational responses...")
+        model_id = "meta-llama/Llama-3.2-1B-Instruct"
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        
+        # Determine compute device for LLM
+        llm_device = "cpu"  # Default to CPU for LLM
+        
+        # Use CUDA if available and there's enough VRAM
+        if device == "cuda" and torch.cuda.is_available():
+            try:
+                free_mem = torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_allocated(0)
+                # If we have at least 2GB free, use CUDA for LLM
+                if free_mem > 2 * 1024 * 1024 * 1024:
+                    llm_device = "cuda"
+            except:
+                pass
+        
+        logger.info(f"Using {llm_device} for Llama 3.2 model")
+        
+        # Load the model with lower precision for efficiency
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id, 
+            torch_dtype=torch.float16 if llm_device == "cuda" else torch.float32,
+            device_map=llm_device
+        )
+        
+        # Create a pipeline for easier inference
+        llm = pipeline(
+            "text-generation",
+            model=model,
+            tokenizer=tokenizer,
+            max_length=512,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            repetition_penalty=1.1
+        )
+        
+        logger.info("Llama 3.2 model loaded successfully")
+        return llm
     except Exception as e:
-                logger.warning(f"CUDA loading failed, staying with CPU model: {e}")
+        logger.error(f"Error loading Llama 3.2 model: {e}")
-    except Exception as e:
+        return None
-        logger.error(f"Error loading WhisperX model: {e}")
-        # Create a minimal dummy model as last resort
-        class DummyModel:
-            def __init__(self):
-                self.device = "cpu"
-            def transcribe(self, *args, **kwargs):
-                return {"segments": [{"text": "Speech recognition currently unavailable."}]}
 
-        asr_model = DummyModel()
+# Load the LLM model
-        logger.warning("Using dummy transcription model - ASR functionality limited")
+llm = load_llm_model()
-    
-    return generator, asr_model
-
-# Load speech models
-generator, asr_model = load_speech_models()
 
 # Set up Flask and Socket.IO
 app = Flask(__name__)
@@ -307,67 +297,117 @@ def encode_audio_data(audio_tensor: torch.Tensor) -> str:
         buf.seek(0)
         return f"data:audio/wav;base64,{base64.b64encode(buf.read()).decode('utf-8')}"
 
-def transcribe_audio(audio_tensor: torch.Tensor) -> str:
+def process_speech(audio_tensor: torch.Tensor, client_id: str) -> str:
-    """Transcribe audio using WhisperX with robust error handling"""
+    """Process speech with speech recognition"""
-    global asr_model
+    if not speech_recognizer:
+        # Fallback to basic detection if model failed to load
+        return detect_speech_energy(audio_tensor)
     
     try:
-        # Save the tensor to a temporary file
+        # Save audio to temp file for Whisper
-        temp_path = os.path.join(base_dir, f"temp_audio_{time.time()}.wav")
+        temp_path = os.path.join(base_dir, f"temp_{time.time()}.wav")
         torchaudio.save(temp_path, audio_tensor.unsqueeze(0).cpu(), generator.sample_rate)
         
-        logger.info(f"Transcribing audio file: {os.path.getsize(temp_path)} bytes")
+        # Perform speech recognition
+        result = speech_recognizer(temp_path)
+        transcription = result["text"]
         
-        # Load the audio for WhisperX
+        # Clean up temp file
-        try:
-            audio = whisperx.load_audio(temp_path)
-        except Exception as e:
-            logger.warning(f"WhisperX load_audio failed: {e}")
-            # Fall back to manual loading
-            import soundfile as sf
-            audio, sr = sf.read(temp_path)
-            if sr != 16000:  # WhisperX expects 16kHz audio
-                from scipy import signal
-                audio = signal.resample(audio, int(len(audio) * 16000 / sr))
-        
-        # Transcribe with error handling
-        try:
-            result = asr_model.transcribe(audio, batch_size=4)
-        except RuntimeError as e:
-            if "CUDA" in str(e) or "libcudnn" in str(e):
-                logger.warning(f"CUDA error in transcription, falling back to CPU: {e}")
-                try:
-                    # Try CPU model
-                    cpu_model = whisperx.load_model("tiny", "cpu", compute_type="int8")
-                    result = cpu_model.transcribe(audio, batch_size=1)
-                    # Update the global model if the original one is broken
-                    asr_model = cpu_model
-                except Exception as cpu_e:
-                    logger.error(f"CPU fallback failed: {cpu_e}")
-                    return "I'm having trouble processing audio right now."
-            else:
-                raise
-        finally:
-            # Clean up
         if os.path.exists(temp_path):
             os.remove(temp_path)
         
-        # Extract text from segments
+        # Return empty string if no speech detected
-        if result["segments"] and len(result["segments"]) > 0:
+        if not transcription or transcription.isspace():
-            transcription = " ".join([segment["text"] for segment in result["segments"]])
+            return "I didn't detect any speech. Could you please try again?"
-            logger.info(f"Transcription: '{transcription.strip()}'")
+        
-            return transcription.strip()
+        return transcription
         
-        return ""
     except Exception as e:
-        logger.error(f"Error in transcription: {e}")
+        logger.error(f"Speech recognition error: {e}")
-        if os.path.exists(temp_path):
+        return "Sorry, I couldn't understand what you said. Could you try again?"
-            os.remove(temp_path)
+
-        return "I heard something but couldn't understand it."
+def detect_speech_energy(audio_tensor: torch.Tensor) -> str:
+    """Basic speech detection based on audio energy levels"""
+    # Calculate audio energy
+    energy = torch.mean(torch.abs(audio_tensor)).item()
+    
+    logger.debug(f"Audio energy detected: {energy:.6f}")
+    
+    # Generate response based on energy level
+    if energy > 0.1:  # Louder speech
+        return "I heard you speaking clearly. How can I help you today?"
+    elif energy > 0.05:  # Moderate speech
+        return "I heard you say something. Could you please repeat that?"
+    elif energy > 0.02:  # Soft speech
+        return "I detected some speech, but it was quite soft. Could you speak up a bit?"
+    else:  # Very soft or no speech
+        return "I didn't detect any speech. Could you please try again?"
 
 def generate_response(text: str, conversation_history: List[Segment]) -> str:
-    """Generate a contextual response based on the transcribed text"""
+    """Generate a contextual response based on the transcribed text using Llama 3.2"""
-    # Simple response logic - can be replaced with a more sophisticated LLM
+    # If LLM is not available, use simple responses
+    if llm is None:
+        return generate_simple_response(text)
+    
+    try:
+        # Create a conversational prompt based on history
+        # Format: recent conversation turns (up to 4) + current user input
+        history_str = ""
+        
+        # Add up to 4 recent conversation turns (excluding the current one)
+        recent_segments = [
+            seg for seg in conversation_history[-8:] 
+            if seg.text and not seg.text.isspace()
+        ]
+        
+        for i, segment in enumerate(recent_segments):
+            speaker_name = "User" if segment.speaker == 0 else "Assistant"
+            history_str += f"{speaker_name}: {segment.text}\n"
+            
+        # Construct the prompt for Llama 3.2
+        prompt = f"""<|system|>
+You are Sesame, a helpful, friendly and concise voice assistant. 
+Keep your responses conversational, natural, and to the point.
+Respond to the user's latest message in the context of the conversation.
+<|end|>
+
+{history_str}
+User: {text}
+Assistant:"""
+
+        logger.debug(f"LLM Prompt: {prompt}")
+        
+        # Generate response with the LLM
+        result = llm(
+            prompt,
+            max_new_tokens=150,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            repetition_penalty=1.1
+        )
+        
+        # Extract the generated text
+        response = result[0]["generated_text"]
+        
+        # Extract just the Assistant's response (after the prompt)
+        response = response.split("Assistant:")[-1].strip()
+        
+        # Clean up and ensure it's not too long for TTS
+        response = response.split("\n")[0].strip()
+        if len(response) > 200:
+            response = response[:197] + "..."
+            
+        logger.info(f"LLM response: {response}")
+        return response
+        
+    except Exception as e:
+        logger.error(f"Error generating LLM response: {e}")
+        # Fall back to simple responses
+        return generate_simple_response(text)
+
+def generate_simple_response(text: str) -> str:
+    """Generate a simple rule-based response as fallback"""
     responses = {
         "hello": "Hello there! How can I help you today?",
         "hi": "Hi there! What can I do for you?",
@@ -394,7 +434,7 @@ def generate_response(text: str, conversation_history: List[Segment]) -> str:
     elif len(text) < 10:
         return "Thanks for your message. Could you elaborate a bit more?"
     else:
-        return f"I understand you said '{text}'. That's interesting! Can you tell me more about that?"
+        return f"I heard you say something about that. Can you tell me more?"
 
 # Flask Routes
 @app.route('/')
@@ -610,33 +650,32 @@ def process_complete_utterance(client_id, client, speaker_id, is_incomplete=Fals
         # Combine audio chunks
         full_audio = torch.cat(client['streaming_buffer'], dim=0)
         
-        # Process with speech-to-text
+        # Process audio to generate a response (no speech recognition)
-        logger.info(f"[{client_id[:8]}] Starting transcription...")
+        generated_text = process_speech(full_audio, client_id)
-        transcribed_text = transcribe_audio(full_audio)
         
         # Add suffix for incomplete utterances
         if is_incomplete:
-            transcribed_text += " (processing continued speech...)"
+            generated_text += " (processing continued speech...)"
         
-        # Log the transcription
+        # Log the generated text
-        logger.info(f"[{client_id[:8]}] Transcribed: '{transcribed_text}'")
+        logger.info(f"[{client_id[:8]}] Generated text: '{generated_text}'")
         
-        # Handle the transcription result
+        # Handle the result
-        if transcribed_text:
+        if generated_text:
             # Add user message to context
-            user_segment = Segment(text=transcribed_text, speaker=speaker_id, audio=full_audio)
+            user_segment = Segment(text=generated_text, speaker=speaker_id, audio=full_audio)
             client['context_segments'].append(user_segment)
             
-            # Send the transcribed text to client
+            # Send the text to client
             emit('transcription', {
                 'type': 'transcription',
-                'text': transcribed_text
+                'text': generated_text
             }, room=client_id)
             
             # Only generate a response if this is a complete utterance
             if not is_incomplete:
                 # Generate a contextual response
-                response_text = generate_response(transcribed_text, client['context_segments'])
+                response_text = generate_response(generated_text, client['context_segments'])
                 logger.info(f"[{client_id[:8]}] Generating response: '{response_text}'")
                 
                 # Let the client know we're processing
@@ -684,7 +723,7 @@ def process_complete_utterance(client_id, client, speaker_id, is_incomplete=Fals
                         'message': "Sorry, there was an error generating the audio response."
                     }, room=client_id)
         else:
-            # If transcription failed, send a notification
+            # If processing failed, send a notification
             emit('error', {
                 'type': 'error',
                 'message': "Sorry, I couldn't understand what you said. Could you try again?"
@@ -791,7 +830,7 @@ if __name__ == "__main__":
     print(f"   - Network URL: http://<your-ip-address>:5000")
     print(f"{'='*60}")
     print(f"🌐 Device: {device.upper()}")
-    print(f"🧠 Models: Sesame CSM + WhisperX ASR")
+    print(f"🧠 Models: Sesame CSM (TTS only)")
     print(f"🔧 Serving from: {os.path.join(base_dir, 'index.html')}")
     print(f"{'='*60}")
     print(f"Ready to receive connections! Press Ctrl+C to stop the server.\n")

React/package-lock.json

@@ -11,7 +11,8 @@
         "@auth0/nextjs-auth0": "^4.3.0",
         "next": "15.2.4",
         "react": "^19.1.0",
-        "react-dom": "^19.1.0"
+        "react-dom": "^19.1.0",
+        "twilio": "^5.5.1"
       },
       "devDependencies": {
         "@skeletonlabs/skeleton": "^3.1.1",
@@ -89,6 +90,111 @@
       "integrity": "sha512-+SFtMgoiYP3WoSswuNmxJOCwi06TdWE733D+WPjpXIe4LXGULwEaofiiAy6kbS0+XjM5xF5n3lKuBwN2SnqD9g==",
       "license": "MIT"
     },
+    "node_modules/@next/swc-darwin-arm64": {
+      "version": "15.2.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-15.2.4.tgz",
+      "integrity": "sha512-1AnMfs655ipJEDC/FHkSr0r3lXBgpqKo4K1kiwfUf3iE68rDFXZ1TtHdMvf7D0hMItgDZ7Vuq3JgNMbt/+3bYw==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ],
+      "engines": {
+        "node": ">= 10"
+      }
+    },
+    "node_modules/@next/swc-darwin-x64": {
+      "version": "15.2.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-15.2.4.tgz",
+      "integrity": "sha512-3qK2zb5EwCwxnO2HeO+TRqCubeI/NgCe+kL5dTJlPldV/uwCnUgC7VbEzgmxbfrkbjehL4H9BPztWOEtsoMwew==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ],
+      "engines": {
+        "node": ">= 10"
+      }
+    },
+    "node_modules/@next/swc-linux-arm64-gnu": {
+      "version": "15.2.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-15.2.4.tgz",
+      "integrity": "sha512-HFN6GKUcrTWvem8AZN7tT95zPb0GUGv9v0d0iyuTb303vbXkkbHDp/DxufB04jNVD+IN9yHy7y/6Mqq0h0YVaQ==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ],
+      "engines": {
+        "node": ">= 10"
+      }
+    },
+    "node_modules/@next/swc-linux-arm64-musl": {
+      "version": "15.2.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-15.2.4.tgz",
+      "integrity": "sha512-Oioa0SORWLwi35/kVB8aCk5Uq+5/ZIumMK1kJV+jSdazFm2NzPDztsefzdmzzpx5oGCJ6FkUC7vkaUseNTStNA==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ],
+      "engines": {
+        "node": ">= 10"
+      }
+    },
+    "node_modules/@next/swc-linux-x64-gnu": {
+      "version": "15.2.4",
+      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-15.2.4.tgz",
+      "integrity": "sha512-yb5WTRaHdkgOqFOZiu6rHV1fAEK0flVpaIN2HB6kxHVSy/dIajWbThS7qON3W9/SNOH2JWkVCyulgGYekMePuw==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ],
+      "engines": {
+        "node": ">= 10"
+      }
+    },
+    "node_modules/@next/swc-linux-x64-musl": {
+      "version": "15.2.4",
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+      "license": "MIT"
+    },
+    "node_modules/lodash.isinteger": {
+      "version": "4.0.4",
+      "resolved": "https://registry.npmjs.org/lodash.isinteger/-/lodash.isinteger-4.0.4.tgz",
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+      "license": "MIT"
+    },
+    "node_modules/lodash.isnumber": {
+      "version": "3.0.3",
+      "resolved": "https://registry.npmjs.org/lodash.isnumber/-/lodash.isnumber-3.0.3.tgz",
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+      "license": "MIT"
+    },
+    "node_modules/lodash.isplainobject": {
+      "version": "4.0.6",
+      "resolved": "https://registry.npmjs.org/lodash.isplainobject/-/lodash.isplainobject-4.0.6.tgz",
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+      "license": "MIT"
+    },
+    "node_modules/lodash.isstring": {
+      "version": "4.0.1",
+      "resolved": "https://registry.npmjs.org/lodash.isstring/-/lodash.isstring-4.0.1.tgz",
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+      "license": "MIT"
+    },
+    "node_modules/lodash.once": {
+      "version": "4.1.1",
+      "resolved": "https://registry.npmjs.org/lodash.once/-/lodash.once-4.1.1.tgz",
+      "integrity": "sha512-Sb487aTOCr9drQVL8pIxOzVhafOjZN9UU54hiN8PU3uAiSV7lx1yYNpbNmex2PK6dSJoNTSJUUswT651yww3Mg==",
+      "license": "MIT"
+    },
+    "node_modules/math-intrinsics": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/math-intrinsics/-/math-intrinsics-1.1.0.tgz",
+      "integrity": "sha512-/IXtbwEk5HTPyEwyKX6hGkYXxM9nbj64B+ilVJnC/R6B0pH5G4V3b0pVbL7DBj4tkhBAppbQUlf6F6Xl9LHu1g==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.4"
+      }
+    },
+    "node_modules/mime-db": {
+      "version": "1.52.0",
+      "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
+      "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/mime-types": {
+      "version": "2.1.35",
+      "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz",
+      "integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==",
+      "license": "MIT",
+      "dependencies": {
+        "mime-db": "1.52.0"
+      },
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/ms": {
+      "version": "2.1.3",
+      "resolved": "https://registry.npmjs.org/ms/-/ms-2.1.3.tgz",
+      "integrity": "sha512-6FlzubTLZG3J2a/NVCAleEhjzq5oxgHyaCU9yYXvcLsvoVaHJq/s5xXI6/XXP6tz7R9xAOtHnSO/tXtF3WRTlA==",
+      "license": "MIT"
+    },
     "node_modules/nanoid": {
       "version": "3.3.11",
       "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.11.tgz",
@@ -883,6 +1431,18 @@
         "url": "https://github.com/sponsors/panva"
       }
     },
+    "node_modules/object-inspect": {
+      "version": "1.13.4",
+      "resolved": "https://registry.npmjs.org/object-inspect/-/object-inspect-1.13.4.tgz",
+      "integrity": "sha512-W67iLl4J2EXEGTbfeHCffrjDfitvLANg0UlX3wFUUSTx92KXRFegMHUVgSqE+wvhAbi4WqjGg9czysTV2Epbew==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
     "node_modules/picocolors": {
       "version": "1.1.1",
       "resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.1.1.tgz",
@@ -925,6 +1485,27 @@
       "dev": true,
       "license": "MIT"
     },
+    "node_modules/proxy-from-env": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/proxy-from-env/-/proxy-from-env-1.1.0.tgz",
+      "integrity": "sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg==",
+      "license": "MIT"
+    },
+    "node_modules/qs": {
+      "version": "6.14.0",
+      "resolved": "https://registry.npmjs.org/qs/-/qs-6.14.0.tgz",
+      "integrity": "sha512-YWWTjgABSKcvs/nWBi9PycY/JiPJqOD4JA6o9Sej2AtvSGarXxKC3OQSk4pAarbdQlKAh5D4FCQkJNkW+GAn3w==",
+      "license": "BSD-3-Clause",
+      "dependencies": {
+        "side-channel": "^1.1.0"
+      },
+      "engines": {
+        "node": ">=0.6"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
     "node_modules/react": {
       "version": "19.1.0",
       "resolved": "https://registry.npmjs.org/react/-/react-19.1.0.tgz",
@@ -946,18 +1527,43 @@
         "react": "^19.1.0"
       }
     },
+    "node_modules/safe-buffer": {
+      "version": "5.2.1",
+      "resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.2.1.tgz",
+      "integrity": "sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==",
+      "funding": [
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/feross"
+        },
+        {
+          "type": "patreon",
+          "url": "https://www.patreon.com/feross"
+        },
+        {
+          "type": "consulting",
+          "url": "https://feross.org/support"
+        }
+      ],
+      "license": "MIT"
+    },
     "node_modules/scheduler": {
       "version": "0.26.0",
       "resolved": "https://registry.npmjs.org/scheduler/-/scheduler-0.26.0.tgz",
       "integrity": "sha512-NlHwttCI/l5gCPR3D1nNXtWABUmBwvZpEQiD4IXSbIDq8BzLIK/7Ir5gTFSGZDUu37K5cMNp0hFtzO38sC7gWA==",
       "license": "MIT"
     },
+    "node_modules/scmp": {
+      "version": "2.1.0",
+      "resolved": "https://registry.npmjs.org/scmp/-/scmp-2.1.0.tgz",
+      "integrity": "sha512-o/mRQGk9Rcer/jEEw/yw4mwo3EU/NvYvp577/Btqrym9Qy5/MdWGBqipbALgd2lrdWTJ5/gqDusxfnQBxOxT2Q==",
+      "license": "BSD-3-Clause"
+    },
     "node_modules/semver": {
       "version": "7.7.1",
       "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.1.tgz",
       "integrity": "sha512-hlq8tAfn0m/61p4BVRcPzIGr6LKiMwo4VM6dGi6pt4qcRkmNzTcWq6eCEjEh+qXjkMDvPlOFFSGwQjoEa6gyMA==",
       "license": "ISC",
-      "optional": true,
       "bin": {
         "semver": "bin/semver.js"
       },
@@ -1005,6 +1611,78 @@
         "@img/sharp-win32-x64": "0.33.5"
       }
     },
+    "node_modules/side-channel": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/side-channel/-/side-channel-1.1.0.tgz",
+      "integrity": "sha512-ZX99e6tRweoUXqR+VBrslhda51Nh5MTQwou5tnUDgbtyM0dBgmhEDtWGP/xbKn6hqfPRHujUNwz5fy/wbbhnpw==",
+      "license": "MIT",
+      "dependencies": {
+        "es-errors": "^1.3.0",
+        "object-inspect": "^1.13.3",
+        "side-channel-list": "^1.0.0",
+        "side-channel-map": "^1.0.1",
+        "side-channel-weakmap": "^1.0.2"
+      },
+      "engines": {
+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
+    "node_modules/side-channel-list": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/side-channel-list/-/side-channel-list-1.0.0.tgz",
+      "integrity": "sha512-FCLHtRD/gnpCiCHEiJLOwdmFP+wzCmDEkc9y7NsYxeF4u7Btsn1ZuwgwJGxImImHicJArLP4R0yX4c2KCrMrTA==",
+      "license": "MIT",
+      "dependencies": {
+        "es-errors": "^1.3.0",
+        "object-inspect": "^1.13.3"
+      },
+      "engines": {
+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
+    "node_modules/side-channel-map": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/side-channel-map/-/side-channel-map-1.0.1.tgz",
+      "integrity": "sha512-VCjCNfgMsby3tTdo02nbjtM/ewra6jPHmpThenkTYh8pG9ucZ/1P8So4u4FGBek/BjpOVsDCMoLA/iuBKIFXRA==",
+      "license": "MIT",
+      "dependencies": {
+        "call-bound": "^1.0.2",
+        "es-errors": "^1.3.0",
+        "get-intrinsic": "^1.2.5",
+        "object-inspect": "^1.13.3"
+      },
+      "engines": {
+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
+    "node_modules/side-channel-weakmap": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/side-channel-weakmap/-/side-channel-weakmap-1.0.2.tgz",
+      "integrity": "sha512-WPS/HvHQTYnHisLo9McqBHOJk2FkHO/tlpvldyrnem4aeQp4hai3gythswg6p01oSoTl58rcpiFAjF2br2Ak2A==",
+      "license": "MIT",
+      "dependencies": {
+        "call-bound": "^1.0.2",
+        "es-errors": "^1.3.0",
+        "get-intrinsic": "^1.2.5",
+        "object-inspect": "^1.13.3",
+        "side-channel-map": "^1.0.1"
+      },
+      "engines": {
+        "node": ">= 0.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/ljharb"
+      }
+    },
     "node_modules/simple-swizzle": {
       "version": "0.2.2",
       "resolved": "https://registry.npmjs.org/simple-swizzle/-/simple-swizzle-0.2.2.tgz",
@@ -1091,6 +1769,24 @@
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       "license": "0BSD"
     },
+    "node_modules/twilio": {
+      "version": "5.5.1",
+      "resolved": "https://registry.npmjs.org/twilio/-/twilio-5.5.1.tgz",
+      "integrity": "sha512-b1gLd2eMsCSCHRerp3GQUedVlz0nCTt5FbyPxDPmMvk5cm6eIPk4ZTp5JNpgucARZgpCB2uUACJbdcidEHAUBA==",
+      "license": "MIT",
+      "dependencies": {
+        "axios": "^1.7.8",
+        "dayjs": "^1.11.9",
+        "https-proxy-agent": "^5.0.0",
+        "jsonwebtoken": "^9.0.2",
+        "qs": "^6.9.4",
+        "scmp": "^2.1.0",
+        "xmlbuilder": "^13.0.2"
+      },
+      "engines": {
+        "node": ">=14.0"
+      }
+    },
     "node_modules/typescript": {
       "version": "5.8.2",
       "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.8.2.tgz",
@@ -1121,109 +1817,13 @@
         "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
       }
     },
-    "node_modules/@next/swc-darwin-arm64": {
+    "node_modules/xmlbuilder": {
-      "version": "15.2.4",
+      "version": "13.0.2",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-15.2.4.tgz",
+      "resolved": "https://registry.npmjs.org/xmlbuilder/-/xmlbuilder-13.0.2.tgz",
-      "integrity": "sha512-1AnMfs655ipJEDC/FHkSr0r3lXBgpqKo4K1kiwfUf3iE68rDFXZ1TtHdMvf7D0hMItgDZ7Vuq3JgNMbt/+3bYw==",
+      "integrity": "sha512-Eux0i2QdDYKbdbA6AM6xE4m6ZTZr4G4xF9kahI2ukSEMCzwce2eX9WlTI5J3s+NU7hpasFsr8hWIONae7LluAQ==",
-      "cpu": [
+      "license": "MIT",
-        "arm64"
-      ],
-      "optional": true,
-      "os": [
-        "darwin"
-      ],
       "engines": {
-        "node": ">= 10"
+        "node": ">=6.0"
-      }
-    },
-    "node_modules/@next/swc-darwin-x64": {
-      "version": "15.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-15.2.4.tgz",
-      "integrity": "sha512-3qK2zb5EwCwxnO2HeO+TRqCubeI/NgCe+kL5dTJlPldV/uwCnUgC7VbEzgmxbfrkbjehL4H9BPztWOEtsoMwew==",
-      "cpu": [
-        "x64"
-      ],
-      "optional": true,
-      "os": [
-        "darwin"
-      ],
-      "engines": {
-        "node": ">= 10"
-      }
-    },
-    "node_modules/@next/swc-linux-arm64-gnu": {
-      "version": "15.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-15.2.4.tgz",
-      "integrity": "sha512-HFN6GKUcrTWvem8AZN7tT95zPb0GUGv9v0d0iyuTb303vbXkkbHDp/DxufB04jNVD+IN9yHy7y/6Mqq0h0YVaQ==",
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-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">= 10"
-      }
-    },
-    "node_modules/@next/swc-linux-arm64-musl": {
-      "version": "15.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-15.2.4.tgz",
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-        "x64"
-      ],
-      "optional": true,
-      "os": [
-        "linux"
-      ],
-      "engines": {
-        "node": ">= 10"
-      }
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-    "node_modules/@next/swc-linux-x64-musl": {
-      "version": "15.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-15.2.4.tgz",
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-      }
-    },
-    "node_modules/@next/swc-win32-arm64-msvc": {
-      "version": "15.2.4",
-      "resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-15.2.4.tgz",
-      "integrity": "sha512-dW0i7eukvDxtIhCYkMrZNQfNicPDExt2jPb9AZPpL7cfyUo7QSNl1DjsHjmmKp6qNAqUESyT8YFl/Aw91cNJJg==",
-      "cpu": [
-        "arm64"
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-      "optional": true,
-      "os": [
-        "win32"
-      ],
-      "engines": {
-        "node": ">= 10"
       }
     }
   }

React/src/app/page.tsx

@@ -16,6 +16,24 @@ export default async function Home() {
 
 	console.log("Session:", session?.user);
 
+	const handleEmergency = async () => {
+		// send texts
+		const response = await fetch("/api/sendMessage", {
+			method: "POST",
+			headers: {
+				"Content-Type": "application/json",
+			},
+			body: JSON.stringify({
+				message: `yo i need help`,
+			}),
+		});
+
+		if (!response.ok) {
+			console.error("Error sending message:", response.statusText);
+			return;
+		}
+	}
+
 	// If no session, show sign-up and login buttons
 	if (!session) {	
 

React/src/pages/api/sendMessage.ts

@@ -0,0 +1,28 @@
+import { NextApiRequest, NextApiResponse } from 'next';
+import twilio from 'twilio';
+
+const accountSid = process.env.TWILIO_ACCOUNT_SID;
+const authToken = process.env.TWILIO_AUTH_TOKEN;
+const client = twilio(accountSid, authToken);
+
+export default async function handler(req: NextApiRequest, res: NextApiResponse) {
+  if (req.method === 'POST') {
+    const { message } = req.body;
+
+    try {
+      const response = await client.messages.create({
+        body: message,
+        from: process.env.TWILIO_PHONE_NUMBER,
+        to: '+18777804236',
+      });
+
+      res.status(200).json({ success: true, sid: response.sid });
+    } catch (error) {
+      console.error('Error sending message:', error);
+      res.status(500).json({ success: false, error: 'Failed to send message' });
+    }
+  } else {
+    res.setHeader('Allow', ['POST']);
+    res.status(405).end(`Method ${req.method} Not Allowed`);
+  }
+}