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This commit is contained in:
SirBlob
2026-04-12 08:49:36 -04:00
parent e6c7ed230b
commit 96272aedb7
22 changed files with 677 additions and 433 deletions
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backend/server.py
+54 -53
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@@ -8,6 +8,7 @@ import requests
import json
import base64
import urllib.request
# import ssl
# ssl._create_default_https_context = ssl._create_unverified_context
@@ -35,14 +36,14 @@ app.add_middleware(
allow_headers=["*"],
)
# MongoDB Setup
import certifi
MONGO_URI = os.getenv("MONGODB_URI", "mongodb://localhost:27017/")
mongo_client = MongoClient(MONGO_URI, tlsCAFile=certifi.where())
db = mongo_client.study_buddy_db
study_rooms_collection = db.study_rooms
# Pydantic models for Study Room Data
class GeoJSONPoint(BaseModel):
type: str = "Point"
coordinates: List[float]
@@ -75,12 +76,12 @@ HEADERS = {
"sec-fetch-site": "same-origin",
"sentry-trace": "250c82a03041415b99422d838ccc7003-9a9ec11d7fd0293b-0",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/146.0.0.0 Safari/537.36 Edg/146.0.0.0",
"x-cosmos-session-281286": "0:-1#10931",
"x-cosmos-session-295334": "0:-1#749854",
"x-cosmos-session-317755": "0:-1#191601",
"x-cosmos-session-382299": "0:-1#265024",
"x-cosmos-session-418988": "0:-1#4058856",
"x-cosmos-session-793952": "0:-1#14004",
"x-cosmos-session-281286": "0:-1",
"x-cosmos-session-295334": "0:-1",
"x-cosmos-session-317755": "0:-1",
"x-cosmos-session-382299": "0:-1",
"x-cosmos-session-418988": "0:-1",
"x-cosmos-session-793952": "0:-1",
"x-request-id": "6a128b8a-7f63-4f97-a40b-bfd31b4a376e",
"x-timezone": "America/New_York",
}
@@ -97,7 +98,7 @@ def _write_wav_to_buffer(pcm_data: bytes, sample_rate: int = SAMPLE_RATE) -> byt
buf.write(b"WAVE")
buf.write(b"fmt ")
buf.write(struct.pack("<I", 16))
buf.write(struct.pack("<H", 1)) # PCM
buf.write(struct.pack("<H", 1))
buf.write(struct.pack("<H", NUM_CHANNELS))
buf.write(struct.pack("<I", sample_rate))
buf.write(struct.pack("<I", byte_rate))
@@ -112,8 +113,8 @@ def _transcribe_pcm(pcm_data: bytes, sample_rate: int = SAMPLE_RATE) -> str:
"""Transcribe raw PCM audio using faster-whisper via a temp WAV file."""
from faster_whisper import WhisperModel
print(f" Using sample rate: {sample_rate} Hz")
# Debug: analyze PCM audio quality
num_samples = len(pcm_data) // 2
if num_samples > 0:
samples = list(struct.unpack(f"<{num_samples}h", pcm_data[:num_samples * 2]))
@@ -121,30 +122,30 @@ def _transcribe_pcm(pcm_data: bytes, sample_rate: int = SAMPLE_RATE) -> str:
mean_s = sum(samples) / num_samples
rms = (sum(s * s for s in samples) / num_samples) ** 0.5
print(f" PCM stats (raw): {num_samples} samples, min={min_s}, max={max_s}, mean={mean_s:.1f}, RMS={rms:.1f}")
# Remove DC offset (center audio at 0)
dc_offset = int(round(mean_s))
samples = [max(-32768, min(32767, s - dc_offset)) for s in samples]
pcm_data = struct.pack(f"<{num_samples}h", *samples)
# Stats after correction
rms_fixed = (sum(s * s for s in samples) / num_samples) ** 0.5
print(f" PCM stats (fixed): DC offset removed={dc_offset}, RMS={rms_fixed:.1f}")
wav_data = _write_wav_to_buffer(pcm_data, sample_rate=sample_rate)
tmp_fd, tmp_path = tempfile.mkstemp(suffix=".wav")
try:
with os.fdopen(tmp_fd, "wb") as f:
f.write(wav_data)
# Save a debug copy so we can listen
debug_path = os.path.join(os.path.dirname(__file__), "debug_audio.wav")
with open(debug_path, "wb") as df:
df.write(wav_data)
print(f" Debug WAV saved to: {debug_path}")
# Initialize the model (using base model for speed)
model = WhisperModel("base", device="cpu", compute_type="int8")
segments, info = model.transcribe(tmp_path, beam_size=5)
seg_list = list(segments)
@@ -190,7 +191,7 @@ def get_terp_ai_response(message: str) -> str:
except Exception as e:
print(f"Terp AI error: {e}")
return "I am sorry, there was an error connecting to Terp AI."
return full_response
def _convert_to_pcm(audio_data: bytes, input_format: str = "mp3") -> bytes | None:
@@ -232,7 +233,7 @@ def _generate_tts(text: str) -> bytes | None:
print("ElevenLabs API key not configured")
return None
# Request PCM directly — no ffmpeg needed
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}?output_format=pcm_16000"
headers = {
@@ -267,7 +268,7 @@ def _generate_tts(text: str) -> bytes | None:
print(f"ElevenLabs TTS error: {e}")
return None
# Latest TTS WAV stored in memory for HTTP download by M5GO
_latest_tts_wav = None
@app.get("/api/tts-audio")
@@ -282,40 +283,40 @@ async def websocket_voice(websocket: WebSocket):
await websocket.accept()
print("Device connected to WebSocket.")
audio_buffer = bytearray()
try:
while True:
data = await websocket.receive()
if "bytes" in data:
audio_buffer.extend(data["bytes"])
elif "text" in data:
try:
msg = json.loads(data["text"])
if msg.get("event") == "stop_listening":
pcm_data = bytes(audio_buffer)
audio_buffer = bytearray() # Reset for next time
audio_buffer = bytearray()
device_sample_rate = msg.get("sample_rate", SAMPLE_RATE)
print(f"Received stop_listening event. Buffer size: {len(pcm_data)} bytes, sample_rate: {device_sample_rate} Hz")
if len(pcm_data) < 3200:
print("Audio too short, ignoring.")
await websocket.send_bytes(b"")
continue
# Step 1: Speech to Text
print("Transcribing...")
user_text = _transcribe_pcm(pcm_data, sample_rate=device_sample_rate)
if not user_text:
print("Transcription failed or empty.")
await websocket.send_text(json.dumps({"event": "error", "msg": "No speech detected"}))
continue
print(f"User said: {user_text}")
# Step 2: Terp AI
print("Sending to Terp AI...")
context_str = get_latest_locations_context()
augmented_prompt = f"USER ASKS: {user_text}\n\n[SYSTEM CONTEXT - LATEST UMD ROOM STATS TO HELP YOU ANSWER IF ASKED]:\n{context_str}"
@@ -324,15 +325,15 @@ async def websocket_voice(websocket: WebSocket):
print("No response from Terp AI.")
await websocket.send_text(json.dumps({"event": "error", "msg": "No AI response"}))
continue
print(f"Terp AI response: {ai_response_text}")
# Step 3: Text to Speech
print("Generating TTS...")
tts_pcm = _generate_tts(ai_response_text)
if tts_pcm:
# Save as WAV for HTTP download by M5GO
global _latest_tts_wav
_latest_tts_wav = _write_wav_to_buffer(tts_pcm)
print(f"TTS WAV ready: {len(_latest_tts_wav)} bytes, serving via /api/tts-audio")
@@ -343,7 +344,7 @@ async def websocket_voice(websocket: WebSocket):
else:
print("TTS failed.")
await websocket.send_text(json.dumps({"event": "error", "msg": "TTS failed"}))
except json.JSONDecodeError:
pass
except Exception as e:
@@ -383,41 +384,41 @@ def get_latest_locations_context() -> str:
}}
]
latest_stats = list(study_rooms_collection.aggregate(pipeline))
if not latest_stats:
return "No recent location noise stats available today."
room_dict = {loc["id"]: loc["name"] for loc in UMD_LOCATIONS}
lines = ["Latest Study Room Stats:"]
for stat in latest_stats:
room_id = stat.get("_id")
name = room_dict.get(room_id, room_id)
db = stat.get("latest_db", 0.0)
status = "Quiet"
if isinstance(db, (int, float)):
if db >= 65: status = "Loud"
elif db >= 55: status = "Moderate"
lines.append(f"- {name}: Noise Level {db:.1f} dB ({status})")
return "\n".join(lines)
@app.post("/api/study-rooms")
async def create_study_room_data(data: StudyRoomData):
# Check if the coordinates match one of the known locations (with small tolerance)
is_valid_location = False
req_lng, req_lat = data.location.coordinates[0], data.location.coordinates[1]
for loc in UMD_LOCATIONS:
if abs(loc["lng"] - req_lng) < 0.0001 and abs(loc["lat"] - req_lat) < 0.0001:
is_valid_location = True
# Override coordinates to exactly match known location for consistency
data.location.coordinates = [loc["lng"], loc["lat"]]
data.room_id = loc["id"]
break
if not is_valid_location:
from fastapi import HTTPException
raise HTTPException(status_code=400, detail="Invalid location. Coordinates must correspond to a known UMD location.")
@@ -438,7 +439,7 @@ async def get_study_room_history():
"""Get all study room data from the last 24 hours."""
twenty_four_hours_ago = datetime.utcnow() - timedelta(hours=24)
rooms = list(study_rooms_collection.find(
{"date": {"$gte": twenty_four_hours_ago}},
{"date": {"$gte": twenty_four_hours_ago}},
{"_id": 0}
).sort("date", -1))
return {"data": rooms}
@@ -448,13 +449,13 @@ async def serve_spa(full_path: str):
static_dir = "static"
if not os.path.exists(static_dir):
return {"error": "Static directory not found. Please build the frontend."}
static_path = os.path.join(static_dir, full_path)
if os.path.isfile(static_path):
return FileResponse(static_path)
index_path = os.path.join(static_dir, "index.html")
if os.path.exists(index_path):
return FileResponse(index_path)
return {"error": "index.html not found in static directory"}