Update app.py

app.py

@@ -8,7 +8,7 @@ import re
 
 # Step 1: Unzip models only once
 unzip_dir = "unzipped_models"
-zip_file = "Models.zip"  # Make sure this is the filename inside your Space repo
+zip_file = "Models.zip"  # Ensure this matches exactly the file name uploaded in your Space repo
 
 if not os.path.exists(unzip_dir):
     print("Extracting model zip file...")
@@ -18,7 +18,7 @@ if not os.path.exists(unzip_dir):
 
 # Step 2: Parse folders to dynamically populate dropdowns
 
-model_root = os.path.join(unzip_dir, 'Models')  # Adjust this if your ZIP structure is different
+model_root = os.path.join(unzip_dir, 'Models')  # Adjust if ZIP structure is different
 
 activations = []
 seeds_dict = dict()
@@ -43,24 +43,16 @@ for act in os.listdir(model_root):
 
 activations = sorted(activations)
 
-
-# Step 3: Load linear models
-# linear_rgb_path = os.path.join(unzip_dir, "linear_models/linear_rgb.joblib")
-# linear_grey_path = os.path.join(unzip_dir, "linear_models/linear_grey.joblib")
-
-# linear_rgb = joblib.load(linear_rgb_path)
-# linear_grey = joblib.load(linear_grey_path)
-
-# Step 4: Prediction function
+# Step 3: Prediction function
 def predict(r, g, b, activation, seed, neurons):
     try:
         X = np.array([[r, g, b]])
         
-        # Linear predictions
+        # Linear prediction (you can replace this with your actual linear model)
         lin_pred_rgb = (1.9221 * r) - (1.3817 * g) + (1.4058 * b) - 0.1318
 
         # ANN prediction
-        keras_path = os.path.join(unzip_dir, activation, seed, f"model_{neurons}.keras")
+        keras_path = os.path.join(model_root, activation, seed, f"model_{neurons}.keras")
         if not os.path.exists(keras_path):
             raise FileNotFoundError(f"Model not found: {keras_path}")
         
@@ -70,16 +62,15 @@ def predict(r, g, b, activation, seed, neurons):
         return ann_pred, lin_pred_rgb
 
     except Exception as e:
-        return f"Error: {str(e)}", "", ""
-
-# Dynamic components for UI
+        return f"Error: {str(e)}", ""
 
+# Step 4: Dynamic UI update functions (Gradio 4.x compliant)
 def update_seeds(activation):
-    return {"choices": seeds_dict[activation], "value": seeds_dict[activation][0]}
+    return gr.update(choices=seeds_dict[activation], value=seeds_dict[activation][0])
 
 def update_neurons(activation, seed):
     neurons = neurons_dict[(activation, seed)]
-    return {"choices": neurons, "value": neurons[0]}
+    return gr.update(choices=neurons, value=neurons[0])
 
 # Gradio Interface
 with gr.Blocks() as demo:
@@ -92,9 +83,9 @@ with gr.Blocks() as demo:
         b = gr.Number(label="B")
 
     with gr.Row():
-        activation = gr.Dropdown(choices=activations, label="Activation Function")
-        seed = gr.Dropdown(label="Seed")
-        neurons = gr.Dropdown(label="Neurons")
+        activation = gr.Dropdown(choices=activations, label="Activation Function", interactive=True)
+        seed = gr.Dropdown(label="Seed", interactive=True)
+        neurons = gr.Dropdown(label="Neurons", interactive=True)
 
     activation.change(update_seeds, inputs=[activation], outputs=[seed])
     seed.change(update_neurons, inputs=[activation, seed], outputs=[neurons])
@@ -105,7 +96,6 @@ with gr.Blocks() as demo:
     with gr.Row():
         ann_output = gr.Text(label="ANN Model Prediction")
         lin_rgb_output = gr.Text(label="Linear RGB Prediction")
-        # lin_grey_output = gr.Text(label="Linear Grey Prediction")
 
     btn.click(
         fn=predict,