Update app.py

app.py

@@ -1,23 +1,22 @@
 from tensorflow.keras.models import load_model
 from tensorflow.keras.initializers import Orthogonal
-from tensorflow.keras.utils import CustomObjectScope
+from tensorflow.keras.utils import custom_object_scope
 from tensorflow.keras.layers import LSTM
 import gradio as gr
+import pandas as pd
 
-lstm_layer = LSTM(64, return_sequences=True, time_major=False)
+# Initialize LSTM layer correctly without time_major
+lstm_layer = LSTM(64, return_sequences=True)
 
-# Register custom initializers or objects
-with CustomObjectScope({'Orthogonal': Orthogonal}):
+# Register custom initializers or objects when loading the model
+with custom_object_scope({'Orthogonal': Orthogonal}):
     model = load_model('models/lstm-combinedmodel.h5')
 
-import pandas as pd
-
 def predict_from_csv(file_path):
-    # Load the data
+    # Load the data from CSV
     data = pd.read_csv(file_path)
     
-    # Assume your model expects data in a specific order and format
-    # Here we reorder the columns if necessary and handle any preprocessing like normalization
+    # Reorder and preprocess data if necessary
     required_columns = ['CAN ID', 'RTR', 'DLC', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7', 'Data8']
     data = data[required_columns]
     
@@ -27,15 +26,13 @@ def predict_from_csv(file_path):
     # Predict using the model
     predictions = model.predict(input_data)
     
-    # Here, you could process the predictions to a more readable format if needed
-    return predictions
+    # Process predictions to readable format if needed
+    return str(predictions)
 
-def interface_func(file_info):
-    # Get the path of the uploaded file
-    filepath = file_info["path"]
-    # Use the prediction function
-    prediction = predict_from_csv(filepath)
-    return prediction
+def interface_func(uploaded_file):
+    # Use the prediction function on the uploaded file path
+    predictions = predict_from_csv(uploaded_file.name)
+    return predictions
 
 iface = gr.Interface(fn=interface_func,
                      inputs=gr.File(label="Upload CSV"),