first commit with independant interface

interface.py

@@ -0,0 +1,95 @@
+import numpy as np
+import pandas as pd
+import gradio as gr
+import plotly.graph_objs as go
+from plotly.subplots import make_subplots
+import datetime
+
+
+# Function to load data from a text file into a numpy array of the right format
+def load_data(filename):
+        with open(filename, 'r') as file:
+            data = [int(line.strip()) for line in file]
+        data = np.array(data, dtype=int)
+        data[data == -100] = -1
+        return data.reshape((-1, 1))
+    
+
+def process_csv(file):
+    
+    if file is None:
+        gr.Warning("No file uploaded") 
+    
+    # Load the data
+    acc_data = np.load(file)
+    predicted_labels = load_data('003_1_R.txt')
+    
+    # Append the new columns
+    complete_array = np.hstack((acc_data, predicted_labels))
+    
+    # Define the time axis
+    duration = 33.333333  # milliseconds
+    num_samples = complete_array.shape[0]
+    time_axis = np.arange(0, num_samples * duration, duration)
+
+    # Convert time axis to datetime objects
+    start_time = datetime.datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
+    time_axis_datetime = [start_time + datetime.timedelta(milliseconds=int(t)) for t in time_axis]
+
+    # Convert datetime objects to formatted strings
+    time_axis_labels = [t.strftime('%H:%M:%S.%f')[:-3] for t in time_axis_datetime]
+    
+    # Calculate the total number of predicted functional and non-functional activity
+    total_predicted_functional = np.sum(complete_array[:, 3] != 0)
+    total_predicted_non_functional = np.sum(complete_array[:, 3] == 0)
+
+    # Calculate percentages
+    predicted_functional_percentage = (total_predicted_functional / len(complete_array)) * 100
+    predicted_non_functional_percentage = (total_predicted_non_functional / len(complete_array)) * 100
+
+    # Formulate return string
+    return_string = f"Percentage of predicted functional activity: {predicted_functional_percentage:.2f}%\nPercentage of predicted non-functional activity: {predicted_non_functional_percentage:.2f}%"
+    
+    # Create subplots
+    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, row_heights=[0.6, 0.4], specs=[[{"type": "scatter"}], [{"type": "scatter"}]])
+
+    # Add traces to the subplots
+    fig.add_trace(go.Scatter(x=time_axis_labels, y=complete_array[:, 0], mode='lines', name='Acc X', line=dict(width=0.75)), row=1, col=1)
+    fig.add_trace(go.Scatter(x=time_axis_labels, y=complete_array[:, 1], mode='lines', name='Acc Y', line=dict(width=0.75)), row=1, col=1)
+    fig.add_trace(go.Scatter(x=time_axis_labels, y=complete_array[:, 2], mode='lines', name='Acc Z', line=dict(width=0.75)), row=1, col=1)
+    fig.add_trace(go.Scatter(x=time_axis_labels, y=complete_array[:, 3], mode='lines', name='Predicted labels', line=dict(width=1)), row=2, col=1)
+
+
+    # Update layout
+    fig.update_layout(
+        title='Accelerometer Data with Annotated Labels',
+        xaxis=dict(title='Time (milliseconds)'),
+        yaxis=dict(title='Accelerometer Data'),
+        yaxis2=dict(title='Predicted'),
+        showlegend=True,
+        height=600
+    )
+
+    return return_string, fig
+
+
+
+with gr.Blocks(theme=gr.themes.Base()) as demo:
+    gr.Markdown(
+    """
+    # Functional Upper Limb Activity Recognition Model
+    Upload your csv file containing accelerometer data to obtain a prediction on the amount of functional activity of the upper limbs.
+    """)
+    with gr.Row(equal_height=True):
+        with gr.Column():
+            input_file = gr.File(label="Upload CSV file")
+            with gr.Row():
+                submit_btn = gr.Button("Submit", variant='primary')
+                clear_btn = gr.ClearButton(input_file, variant='secondary')
+        output_text = gr.Textbox(label="Prediction statistics")
+    output_plot = gr.Plot(label="CSV Plot")
+    
+    submit_btn.click(fn=process_csv, inputs=input_file, outputs=[output_text, output_plot])
+    clear_btn.click(fn=lambda: input_file.reinit())
+
+demo.launch()