done

app.py

@@ -62,62 +62,72 @@ from tensorflow.keras.layers import LSTM
 with custom_object_scope({'Orthogonal': Orthogonal}):
     model = load_model('models/lstm-combinedmodel.h5')
 
-# Function to parse the uploaded file from Gradio
-def parse_text_file(file):
-    # Gradio uploads files as a 'werkzeug.datastructures.FileStorage' object
-    # which acts like a file object. Reading it directly if .read() doesn't work.
-    # We need to check the type and handle it accordingly.
-    if hasattr(file, 'read'):
-        content = file.read()
-    elif hasattr(file, 'file'):
-        # In case Gradio passes the FileStorage object where the file is accessible via '.file'
-        content = file.file.read()
+# Function to parse the uploaded file into a DataFrame
+def parse_text_file(uploaded_file):
+    # Initialize content variable
+    content = None
+    
+    # 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.")
-        
-    if isinstance(content, bytes):  # Check if the content is bytes, decode it
+    
+    # If content is in bytes, decode it to a string
+    if isinstance(content, bytes):
         content = content.decode('utf-8')
-
-    file_like = StringIO(content)  # Convert string to a file-like object
+    
+    # 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 file-like object
+    # Process each line from the file-like object
     for line in file_like:
-        line = line.strip().split()  # Split line into parts, removing newlines and extra spaces
+        line = line.strip().split()  # Split line and strip extra whitespace
         if 'Timestamp:' in line:
-            line.remove('Timestamp:')  # Remove specific label
+            line.remove('Timestamp:')
         if 'ID:' in line:
             line.remove('ID:')
         if 'DLC:' in line:
             line.remove('DLC:')
         
-        # Ensure line has enough elements to extract a key and value
+        # Extract timestamp as key and remaining data as value
         if len(line) > 2:
             key = float(line[0])
             value = line[1:]
-            dfdict[key] = value  # Add to dictionary
+            dfdict[key] = value
     
     # 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 index name
+    df.index.name = 'Timestamp'  # Set the index name
     return df
 
+
+# Gradio interface function
 def interface_func(uploaded_file):
     # Parse the text file into a DataFrame
     df = parse_text_file(uploaded_file)
     
-    # Optionally process the DataFrame further or directly show it
-    return df.to_html()
+    # Return the DataFrame as an HTML table for visualization
+    return df.to_html()  # You can also return other outputs if needed
+
 
 # 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 convert it to a DataFrame."
+    description="Upload a text file with CAN data to visualize the DataFrame."
 )
 
 # Launch the interface
-iface.launch()
+iface.launch()