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README.md

@@ -1,13 +1,19 @@
 ---
-title: DeepLearningAssignment2
+title: Wearable Activity Classifier
 emoji: 📈
-colorFrom: yellow
-colorTo: purple
+colorFrom: blue
+colorTo: gray
 sdk: gradio
-sdk_version: 6.26.0
-python_version: '3.12'
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Wearable Activity Classifier
+
+Beginner Deep Learning group-lab demo. The Space loads a student-trained Keras model and classifies a CSV of 100 sensor readings into Stationary, Walking, or Running.
+
+## Required files
+Upload `activity_model.keras` from the completed notebook together with `app.py`, `requirements.txt`, and the sample CSV files.
+
+## Educational note
+The training data are synthetic and intentionally simple. This demo is for learning model architecture and deployment workflow, not real-world activity recognition.

app.py

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+import numpy as np
+import pandas as pd
+import gradio as gr
+import tensorflow as tf
+
+MODEL_PATH = "activity_model.keras"
+CLASS_NAMES = ["Stationary", "Walking", "Running"]
+EXPECTED_STEPS = 100
+
+model = tf.keras.models.load_model(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
+
+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()

requirements.txt

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+tensorflow-cpu>=2.16,<3
+pandas>=2.0
+numpy>=1.26
+gradio>=5,<7