Update app.py

app.py

@@ -1,9 +1,10 @@
 import numpy as np
-from tensorflow.keras.models import load_model
+import pickle
 import gradio as gr
 
-# Load the saved .h5 model
-model = load_model('model8346.h5')
+# Load the saved pickle model
+with open('model8346.pkl', 'rb') as f:
+    model = pickle.load(f)
 
 # Define action mapping
 action_map = {
@@ -18,50 +19,41 @@ action_map = {
 # 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, e1, e1, e2, e2, e2, e3, e3, e3, 
-                             e4, e4, e4, e5, e5, e5, e6, e6, e6, 
-                             e7, e7, e7, e8, e8, e8]])
+    input_data_reshaped = np.array(input_data).reshape(1, -1)
+    predicted_label = loaded_model.predict(input_data_reshaped)[0]
+    return action_map.get(predicted_class[0], "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
+    """)
     
-    # Get model prediction
-    prediction = model.predict(input_data)
+    with gr.Row():
+        inputs = [gr.Number(label=f"Feature {i+1}", interactive=True) for i in range(8)]
     
-    # Get predicted class
-    predicted_class = np.argmax(prediction, axis=-1)
+    output = gr.Textbox(label="Prediction", interactive=False)
     
-    return action_map.get(predicted_class[0] + 1, "Unknown action")
-
-# Define Gradio inputs
-inputs = [
-    gr.Number(label="e1"),
-    gr.Number(label="e2"),
-    gr.Number(label="e3"),
-    gr.Number(label="e4"),
-    gr.Number(label="e5"),
-    gr.Number(label="e6"),
-    gr.Number(label="e7"),
-    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],
-]
-
-# Gradio interface
-iface = gr.Interface(
-    fn=action,
-    inputs=inputs,
-    outputs=output,
-    title="ML Model Predictor",
-    examples=examples,
-    flagging_options=["Working", "Not Working"],
-    description="Enter the 8 feature values to get a prediction."
-)
+    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)