done

app.py

@@ -1,133 +1,52 @@
-# from tensorflow.keras.models import load_model
-# from tensorflow.keras.initializers import Orthogonal
-# from tensorflow.keras.utils import custom_object_scope
-# from tensorflow.keras.layers import LSTM
-# import gradio as gr
-# import pandas as pd
-# import numpy as np
-# # Initialize LSTM layer correctly without time_major
-# lstm_layer = LSTM(64, return_sequences=True)
-
-# # Register custom initializers or objects when loading the model
-# with custom_object_scope({'Orthogonal': Orthogonal}):
-#     model = load_model('models/lstm-combinedmodel.h5')
-
-# def predict_from_csv(file_path):
-#     # Load the data from CSV
-#     data = pd.read_csv(file_path)
-    
-#     # Reorder and preprocess data if necessary
-#     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)
-    
-#       # Determine the predicted class and confidence
-#     predicted_class = np.argmax(predictions, axis=1)[0]
-#     confidence = np.max(predictions, axis=1)[0]
-
-#     # Map numeric class to label
-#     class_labels = {0: "Normal", 1: "Anomaly"}
-#     label = class_labels[predicted_class]
-#     output = f"Predicted Class: {label}, Confidence: {confidence:.4f}"
-    
-#     return output
-
-# 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"),
-#                      outputs="text",
-#                      description="Upload a CSV file with the specified columns to predict.")
-
-# iface.launch()
-
-import pandas as pd
-import gradio as gr
-import numpy as np
-from io import StringIO
 from tensorflow.keras.models import load_model
 from tensorflow.keras.initializers import Orthogonal
 from tensorflow.keras.utils import custom_object_scope
 from tensorflow.keras.layers import LSTM
+import gradio as gr
+import pandas as pd
+import numpy as np
+# Initialize LSTM layer correctly without time_major
+lstm_layer = LSTM(64, return_sequences=True)
 
-# Initialize and load the model
+# Register custom initializers or objects when loading the model
 with custom_object_scope({'Orthogonal': Orthogonal}):
     model = load_model('models/lstm-combinedmodel.h5')
 
-# Function to parse the uploaded file into a DataFrame
-def parse_text_file(uploaded_file):
-    # Initialize content variable
-    content = None
+def predict_from_csv(file_path):
+    # Load the data from CSV
+    data = pd.read_csv(file_path)
     
-    # Determine how to get content from the uploaded file
-    if hasattr(uploaded_file, 'read'):
-        content = uploaded_file.read()  # Most common case
-    elif hasattr(uploaded_file, 'file'):
-        # If the file is stored in a `file` attribute
-        content = uploaded_file.file.read()
-    elif hasattr(uploaded_file, 'getvalue'):
-        # In case it's a different object, like a StringIO
-        content = uploaded_file.getvalue()
-    else:
-        # If none of these work, raise an error
-        raise ValueError("The file format provided is not supported.")
+    # Reorder and preprocess data if necessary
+    required_columns = ['CAN ID', 'RTR', 'DLC', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7', 'Data8']
+    data = data[required_columns]
     
-    # If content is in bytes, decode it to a string
-    if isinstance(content, bytes):
-        content = content.decode('utf-8')
+    # Convert data to numpy array or the format your model expects
+    input_data = data.values
     
-    # Create a file-like object from the content
-    file_like = StringIO(content)
-
-    # Dictionary to hold data extracted from text file
-    dfdict = {}
-
-    # Process each line from the file-like object
-    for line in file_like:
-        line = line.strip().split()  # Split line and strip extra whitespace
-        if 'Timestamp:' in line:
-            line.remove('Timestamp:')
-        if 'ID:' in line:
-            line.remove('ID:')
-        if 'DLC:' in line:
-            line.remove('DLC:')
-        
-        # Extract timestamp as key and remaining data as value
-        if len(line) > 2:
-            key = float(line[0])
-            value = line[1:]
-            dfdict[key] = value
+    # Predict using the model
+    predictions = model.predict(input_data)
     
-    # Convert dictionary to DataFrame
-    df = pd.DataFrame.from_dict(dfdict, orient='index', columns=['CAN ID', 'RTR', 'DLC', 'Data1', 'Data2', 'Data3', 'Data4', 'Data5', 'Data6', 'Data7', 'Data8'])
-    df.index.name = 'Timestamp'  # Set the index name
-    return df
-
+      # Determine the predicted class and confidence
+    predicted_class = np.argmax(predictions, axis=1)[0]
+    confidence = np.max(predictions, axis=1)[0]
 
-# Gradio interface function
-def interface_func(uploaded_file):
-    # Parse the text file into a DataFrame
-    df = parse_text_file(uploaded_file)
+    # Map numeric class to label
+    class_labels = {0: "Normal", 1: "Anomaly"}
+    label = class_labels[predicted_class]
+    output = f"Predicted Class: {label}, Confidence: {confidence:.4f}"
     
-    # Return the DataFrame as an HTML table for visualization
-    return df.to_html()  # You can also return other outputs if needed
+    return output
 
+def interface_func(uploaded_file):
+    # Use the prediction function on the uploaded file path
+    predictions = predict_from_csv(uploaded_file.name)
+    return predictions
 
-# Set up the Gradio interface
-iface = gr.Interface(
-    fn=interface_func,
-    inputs=gr.File(label="Upload a text file"),
-    outputs="html",
-    description="Upload a text file with CAN data to visualize the DataFrame."
-)
+iface = gr.Interface(fn=interface_func,
+                     inputs=gr.File(label="Upload CSV"),
+                     outputs="text",
+                     description="Upload a CSV file with the specified columns to predict.")
 
-# Launch the interface
 iface.launch()
+
+