Update app.py

app.py

@@ -1,8 +1,11 @@
-import pandas as pd
 import numpy as np
 from tensorflow.keras.models import load_model
 import gradio as gr
 
+# Load the saved .h5 model
+model = load_model('model8346.h5')
+
+# Define action mapping
 action_map = {
     1: "Hand at rest",
     2: "Hand clenched in a fist",
@@ -12,13 +15,22 @@ action_map = {
     6: "Ulnar deviations",
 }
 
+# Function to process inputs and get a prediction
 def action(e1, e2, e3, e4, e5, e6, e7, e8):
-  model = load_model('model6839.keras')
-  input_data = np.array([[e1, e2, e3, e4, e5, e6, e7, e8]])
-  prediction = model.predict(input_data)
-  predicted_class = np.argmax(prediction, axis=-1)
-  return action_map.get(predicted_class[0]+1, "Unknown action")
+    # Duplicate each value 3 times to create a 24-length input
+    input_data = np.array([[e1, e1, e1, e2, e2, e2, e3, e3, e3, 
+                             e4, e4, e4, e5, e5, e5, e6, e6, e6, 
+                             e7, e7, e7, e8, e8, e8]])
+    
+    # Get model prediction
+    prediction = model.predict(input_data)
+    
+    # Get predicted class
+    predicted_class = np.argmax(prediction, axis=-1)
+    
+    return action_map.get(predicted_class[0] + 1, "Unknown action")
 
+# Define Gradio inputs
 inputs = [
     gr.Number(label="e1"),
     gr.Number(label="e2"),
@@ -30,26 +42,26 @@ inputs = [
     gr.Number(label="e8"),
 ]
 
+# Define Gradio output
 output = gr.Textbox(label="Prediction")
 
+# Example inputs (same as before)
 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],
 ]
 
-def func(e1, e2, e3, e4, e5, e6, e7, e8):
-  return action(e1, e2, e3, e4, e5, e6, e7, e8)
-
+# Gradio interface
 iface = gr.Interface(
-    fn=func,
+    fn=action,
     inputs=inputs,
     outputs=output,
     title="ML Model Predictor",
     examples=examples,
-    flagging_options=["Working", "Not Wotking"],
+    flagging_options=["Working", "Not Working"],
     description="Enter the 8 feature values to get a prediction."
 )
 
-#iface.launch(share=True, auth=('emg','emg123'), auth_message="Type in your <strong>login credentials</strong>")
-iface.launch(share=True)
+# Launch Gradio UI
+iface.launch(share=True, debug=True)