init

app.py

@@ -0,0 +1,37 @@
+from tensorflow.keras.models import load_model
+
+# Load your trained model
+model = load_model('path_to_your_model.h5')
+import pandas as pd
+
+def predict_from_csv(file_path):
+    # Load the data
+    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
+    required_columns = ['CAN ID', 'RTR', 'DLC', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7', 'Data8']
+    data = data[required_columns]
+    
+    # Convert data to numpy array or the format your model expects
+    input_data = data.values
+    
+    # Predict using the model
+    predictions = model.predict(input_data)
+    
+    # Here, you could process the predictions to a more readable format if needed
+    return 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
+
+iface = gr.Interface(fn=interface_func,
+                     inputs=gr.inputs.File(label="Upload CSV"),
+                     outputs="text",
+                     description="Upload a CSV file with the specified columns to predict.")
+
+iface.launch()