File size: 2,196 Bytes
b1ef68c b69ca2b b1ef68c 23778bd b1ef68c b69ca2b b1ef68c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | import numpy as np
import pandas as pd
import gradio as gr
import tensorflow as tf
import spaces
CLASS_NAMES = ["Stationary", "Walking", "Running"]
EXPECTED_STEPS = 100
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense
MODEL_PATH = "activity_model.weights.h5"
model = Sequential([
Conv1D(32, kernel_size=5, activation="relu", input_shape=(100, 1)),
MaxPooling1D(pool_size=2),
Flatten(),
Dense(32, activation="relu"),
Dense(3, activation="softmax")
])
model.load_weights(MODEL_PATH)
def read_signal(file_path):
if file_path is None:
raise gr.Error("Please upload a CSV file.")
df = pd.read_csv(file_path)
# Accept either a column named sensor_value or the first numeric column.
if "sensor_value" in df.columns:
values = df["sensor_value"].to_numpy(dtype=np.float32)
else:
numeric = df.select_dtypes(include=["number"])
if numeric.shape[1] == 0:
raise gr.Error("CSV must contain a numeric sensor column.")
values = numeric.iloc[:, 0].to_numpy(dtype=np.float32)
if len(values) != EXPECTED_STEPS:
raise gr.Error(f"Expected exactly {EXPECTED_STEPS} sensor readings, found {len(values)}.")
return values
@spaces.GPU
def predict_activity(file_path):
values = read_signal(file_path)
x = values.reshape(1, EXPECTED_STEPS, 1)
probs = model.predict(x, verbose=0)[0]
return {name: float(prob) for name, prob in zip(CLASS_NAMES, probs)}
with gr.Blocks() as demo:
gr.Markdown("# Wearable Activity Classifier")
gr.Markdown("Upload a CSV containing exactly 100 sensor readings. The student-trained model predicts Stationary, Walking, or Running.")
file_input = gr.File(label="Sensor CSV", file_types=[".csv"], type="filepath")
output = gr.Label(label="Prediction", num_top_classes=3)
button = gr.Button("Predict activity", variant="primary")
button.click(predict_activity, inputs=file_input, outputs=output)
gr.Examples(
examples=[["sample_stationary.csv"], ["sample_walking.csv"], ["sample_running.csv"]],
inputs=file_input,
label="Sample files"
)
demo.launch() |