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Parent(s):
59d907e
Update app.py
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app.py
CHANGED
@@ -1,23 +1,22 @@
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from tensorflow.keras.models import load_model
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from tensorflow.keras.initializers import Orthogonal
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from tensorflow.keras.utils import
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from tensorflow.keras.layers import LSTM
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import gradio as gr
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# Register custom initializers or objects
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with
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model = load_model('models/lstm-combinedmodel.h5')
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import pandas as pd
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def predict_from_csv(file_path):
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# Load the data
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data = pd.read_csv(file_path)
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#
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# Here we reorder the columns if necessary and handle any preprocessing like normalization
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required_columns = ['CAN ID', 'RTR', 'DLC', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7', 'Data8']
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data = data[required_columns]
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@@ -27,15 +26,13 @@ def predict_from_csv(file_path):
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# Predict using the model
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predictions = model.predict(input_data)
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#
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return predictions
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def interface_func(
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#
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prediction = predict_from_csv(filepath)
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return prediction
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iface = gr.Interface(fn=interface_func,
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inputs=gr.File(label="Upload CSV"),
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from tensorflow.keras.models import load_model
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from tensorflow.keras.initializers import Orthogonal
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from tensorflow.keras.utils import custom_object_scope
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from tensorflow.keras.layers import LSTM
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import gradio as gr
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import pandas as pd
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# Initialize LSTM layer correctly without time_major
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lstm_layer = LSTM(64, return_sequences=True)
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# Register custom initializers or objects when loading the model
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with custom_object_scope({'Orthogonal': Orthogonal}):
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model = load_model('models/lstm-combinedmodel.h5')
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def predict_from_csv(file_path):
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# Load the data from CSV
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data = pd.read_csv(file_path)
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# Reorder and preprocess data if necessary
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required_columns = ['CAN ID', 'RTR', 'DLC', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7', 'Data8']
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data = data[required_columns]
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# Predict using the model
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predictions = model.predict(input_data)
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# Process predictions to readable format if needed
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return str(predictions)
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def interface_func(uploaded_file):
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# Use the prediction function on the uploaded file path
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predictions = predict_from_csv(uploaded_file.name)
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return predictions
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iface = gr.Interface(fn=interface_func,
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inputs=gr.File(label="Upload CSV"),
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