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feat: Calculate and log prediction confidence

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Co-authored-by: aider (gemini/gemini-2.5-pro-preview-05-06) <aider@aider.chat>
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Tanner Collin
2025-07-31 18:34:56 -06:00
parent e9b4fbc757
commit 1bef792b22
1 changed files with 8 additions and 5 deletions
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13
server.py
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@@ -6,6 +6,7 @@ import os
import io import io
import torch import torch
import torch.nn.functional as F
from torchvision import transforms from torchvision import transforms
from PIL import Image from PIL import Image
@@ -46,8 +47,9 @@ def get_prediction(model, image_bytes, device):
with torch.no_grad(): with torch.no_grad():
output = model(input_tensor) output = model(input_tensor)
_, pred_idx = torch.max(output, 1) probabilities = F.softmax(output, dim=1)
return CLASS_NAMES[pred_idx.item()] confidence, pred_idx = torch.max(probabilities, 1)
return CLASS_NAMES[pred_idx.item()], confidence.item()
# --- Background Task --- # --- Background Task ---
async def monitor_garage_door(app): async def monitor_garage_door(app):
@@ -65,9 +67,10 @@ async def monitor_garage_door(app):
async with session.get(CAMERA_URL, headers=headers, timeout=REQUEST_TIMEOUT_SECONDS) as response: async with session.get(CAMERA_URL, headers=headers, timeout=REQUEST_TIMEOUT_SECONDS) as response:
if response.status == 200: if response.status == 200:
image_bytes = await response.read() image_bytes = await response.read()
prediction = get_prediction(model, image_bytes, device) result = get_prediction(model, image_bytes, device)
if prediction: if result:
logging.debug(f"Garage door status: {prediction}") prediction, confidence = result
logging.debug(f"Garage door status: {prediction} (confidence: {confidence:.4f})")
else: else:
logging.error(f"Failed to fetch image. Status: {response.status}, Reason: {response.reason}") logging.error(f"Failed to fetch image. Status: {response.status}, Reason: {response.reason}")