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import numpy as np
import pickle
import gradio as gr
# Load the saved pickle model
with open('model-r.pkl', 'rb') as f:
model = pickle.load(f)
# Define action mapping
action_map = {
0: "CLASS0ACTION",
1: "Hand at rest",
2: "Hand clenched in a fist",
3: "Wrist flexion",
4: "Wrist extension",
5: "Radial deviations",
6: "Ulnar deviations",
}
# Function to process inputs and get a prediction
def action(e1, e2, e3, e4, e5, e6, e7, e8):
# Duplicate each value 3 times to create a 24-length input
input_data = np.array([e1, e2, e3, e4, e5, e6, e7, e8])
input_data_reshaped = input_data.reshape(1, -1)
predicted_label = model.predict(input_data_reshaped)[0]
return action_map.get(predicted_label, "Unknown action")
# Define Gradio UI with improved styling
with gr.Blocks(theme=gr.themes.Soft()) as iface:
gr.Markdown("""
# 🤖 ML Model Predictor
### Enter the 8 feature values below to get a prediction
""")
with gr.Row():
inputs = [gr.Number(label=f"Feature {i+1}", interactive=True) for i in range(8)]
output = gr.Textbox(label="Prediction", interactive=False)
submit_btn = gr.Button("🔍 Predict")
submit_btn.click(action, inputs=inputs, outputs=output)
gr.Examples(
examples=[
[-2.00e-05, 1.00e-05, 2.20e-04, 1.80e-04, -1.50e-04, -5.00e-05, 1.00e-05, 0],
[1.60e-04, -1.00e-04, -2.40e-04, 2.00e-04, 1.00e-04, -9.00e-05, -5.00e-05, -5.00e-05],
[-1.00e-05, 1.00e-05, 1.00e-05, 0, -2.00e-05, 0, -3.00e-05, -3.00e-05],
],
inputs=inputs,
label="Try with Example Inputs"
)
gr.Markdown("""
### 🔍 How it Works:
- Enter values for the 8 features.
- Click the **Predict** button.
- The model will analyze the input and classify the hand motion.
""")
# Launch Gradio UI
iface.launch(share=True, debug=True)
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