Please produce python code for a raspberry pi which will read a file containing lines like the following one and produce a line chart graph with 6 lines labeled "Upper", "Middle", "South", "Lower", "Side", and "East": 

250628144138  14.8  223 15.1 9.8  207 21.2 10.5  238 22.7 8.8  136 15.4 10.2  334 32.8 9.7  184 18.9

Data items are separated by one or more spaces. The 7th and 8th characters in the first data item are the hour of the day ("00" - "23") and the 9th and 10th are the time in minutes. These make up the horizontal axis of the graph. Note that the horizontal spacing must account for 60 minutes to the hour, not 100.

The numbers to be plotted for each time slot are the 3rd, 6th, 9th, 12th, 15th, and 18th data items.

The file to be read will be named "/home/nn/panels.txt". The output file will be named "/home/nn/panels.html".

import plotly.graph_objects as go

# --- Configuration ---
# Input file containing the data
input_file_path = "/home/nn/panels.txt"

# Output HTML file for the graph
output_file_path = "/home/nn/panels.html"

# --- Data Processing ---

# Lists to store the parsed data from the file
# The x_axis will store time in a format Plotly understands (HH:MM)
x_axis_times = []

# Dictionaries to hold the data for each of the 6 lines
plot_data = {
   "Upper": [],
   "Middle": [],
   "South": [],
   "Lower": [],
   "Side": [],
   "East": []
}

print(f"Attempting to read data from: {input_file_path}")

try:
   with open(input_file_path, 'r') as f:
       for line_num, line in enumerate(f, 1):
           # Skips empty lines
           if not line.strip():
               continue

           # Split the line into parts. Handles one or more spaces.
           parts = line.split()

           # A valid line must have at least 18 data items
           if len(parts) < 18:
               print(f"Warning: Skipping malformed line #{line_num}. Not enough data items.")
               continue

           try:
               # --- 1. Extract Time for the X-axis ---
               timestamp_str = parts[0]
               if len(timestamp_str) < 10:
                   print(f"Warning: Skipping malformed line #{line_num}. Timestamp is too short.")
                   continue
               
               # Extract hour (characters 7-8) and minute (characters 9-10)
               hour = int(timestamp_str[6:8])
               minute = int(timestamp_str[8:10])
               
               # Format as "HH:MM". This ensures the chart handles time correctly
               # (e.g., it knows 60 minutes are in an hour).
               x_axis_times.append(f"{hour:02d}:{minute:02d}")

               # --- 2. Extract Data for the Y-axis ---
               # The values to be plotted are the 3rd, 6th, 9th, 12th, 15th, and 18th items.
               # Python list indices are 0-based, so we access parts[2], parts[5], etc.
               plot_data["Upper"].append(float(parts[2]))
               plot_data["Middle"].append(float(parts[5]))
               plot_data["South"].append(float(parts[8]))
               plot_data["Lower"].append(float(parts[11]))
               plot_data["Side"].append(float(parts[14]))
               plot_data["East"].append(float(parts[17]))

           except (ValueError, IndexError) as e:
               print(f"Warning: Skipping malformed line #{line_num}. Error processing data: {e}")
               continue

except FileNotFoundError:
   print(f"Error: The input file was not found at {input_file_path}")
   print("Please make sure the file exists and the path is correct.")
   exit()

# --- Graph Creation ---

if not x_axis_times:
   print("Error: No valid data was read from the file. Cannot generate a graph.")
   exit()
   
print(f"Successfully processed {len(x_axis_times)} data points.")
print("Generating the line chart...")

# Create a new figure
fig = go.Figure()

# Add each of the 6 lines (traces) to the figure
fig.add_trace(go.Scatter(x=x_axis_times, y=plot_data["Upper"], mode='lines', name='Upper'))
fig.add_trace(go.Scatter(x=x_axis_times, y=plot_data["Middle"], mode='lines', name='Middle'))
fig.add_trace(go.Scatter(x=x_axis_times, y=plot_data["South"], mode='lines', name='South'))
fig.add_trace(go.Scatter(x=x_axis_times, y=plot_data["Lower"], mode='lines', name='Lower'))
fig.add_trace(go.Scatter(x=x_axis_times, y=plot_data["Side"], mode='lines', name='Side'))
fig.add_trace(go.Scatter(x=x_axis_times, y=plot_data["East"], mode='lines', name='East'))

# --- Final Touches ---

# Update the layout with titles and axis labels
fig.update_layout(
   title='Panel Data Readings Over Time',
   xaxis_title='Time of Day (HH:MM)',
   yaxis_title='Data Value',
   legend_title='Panel Location',
   font=dict(
       family="Courier New, monospace",
       size=14,
       color="RebeccaPurple"
   )
)

# By providing time as "HH:MM" strings, Plotly automatically sets the
# x-axis type to 'category' or 'date', correctly spacing the time values
# and solving the "60 minutes per hour" requirement.

# --- Output ---

# Write the figure to an HTML file
try:
   fig.write_html(output_file_path)
   print(f"Success! Graph has been saved to: {output_file_path}")
except Exception as e:
   print(f"Error: Could not write the output file. {e}")

250628134452  14.9   230 15.4 9.8   212 21.6 10.5   249 23.6 8.8   134 15.2 10.2   336 32.9 9.8   188 19.2
250628134752  14.5   205 14.2 9.7   194 19.9 10.4   220 21.1 8.8   125 14.2 10.1   293 29.1 9.7   172 17.7
250628135254  14.6  212 14.5 9.8  199 20.4 10.4  223 21.3 8.8  126 14.3 10.1  287 28.5 9.7  177 18.2
250628135554  14.5  206 14.2 9.7  193 19.8 10.4  219 21.0 8.8  125 14.2 10.1  293 29.1 9.7  172 17.7
250628135854  14.2  190 13.3 9.7  180 18.5 10.4  204 19.7 8.8  120 13.6 10.0  282 28.1 9.7  160 16.5
250628140154  13.7  161 11.7 9.6  156 16.1 10.3  177 17.2 8.7  105 12.1 10.0  261 26.2 9.6  139 14.5
250628140454  14.0  175 12.5 9.7  166 17.2 10.4  193 18.6 8.7  108 12.3 10.0  279 27.8 9.6  148 15.3
250628140754  14.2  187 13.2 9.7  176 18.2 10.4  205 19.7 8.8  118 13.4 10.1  301 29.8 9.7  157 16.2
250628141054  14.0  176 12.5 9.7  168 17.3 10.4  193 18.7 8.8  110 12.6 10.0  276 27.5 9.6  149 15.4
250628141354  14.4  201 13.9 9.7  190 19.4 10.4  216 20.7 8.8  118 13.5 10.1  289 28.7 9.7  167 17.3
250628141654  14.8  226 15.2 9.8  211 21.5 10.5  237 22.6 8.8  132 14.9 10.1  310 30.6 9.8  186 19.1
250628141954  14.6  211 14.5 9.8  198 20.3 10.5  224 21.4 8.8  127 14.4 10.1  308 30.5 9.7  177 18.1
250628142254  15.1  243 16.1 9.8  226 22.9 10.6  255 24.2 8.8  140 15.8 10.2  344 33.7 9.8  201 20.5
250628142724  16.0  307 19.2 10.0  288 28.7 10.7  321 29.9 8.9  178 19.9 10.4  424 40.8 10.0  257 25.8
250628143024  16.1  312 19.4 10.0  293 29.3 10.8  327 30.4 8.9  178 19.9 10.4  414 39.9 10.0  262 26.3
250628143324  16.0  308 19.2 10.0  291 29.0 10.8  330 30.6 8.9  182 20.3 10.4  420 40.4 10.0  259 26.0
250628143624  15.3  259 16.9 9.9  242 24.5 10.6  276 26.0 8.9  156 17.6 10.3  378 36.8 9.8  216 21.9
250628144438  14.2  187 13.2 9.7  178 18.3 10.4  203 19.6 8.8  119 13.5 10.1  293 29.1 9.7  158 16.3
250628144738  14.0  177 12.6 9.7  170 17.5 10.3  192 18.5 8.8  112 12.8 10.0  276 27.5 9.6  150 15.6
250628145038  13.6  154 11.3 9.6  150 15.7 10.3  170 16.5 8.7  101 11.5 9.9  242 24.3 9.6  134 14.0
250628145338  13.8  164 11.9 9.6  159 16.5 10.3  178 17.3 8.7  104 11.9 9.9  245 24.6 9.6  141 14.7
250628145638  14.0  176 12.6 9.7  169 17.5 10.3  188 18.2 8.8  112 12.7 10.0  253 25.3 9.6  150 15.6
250628145938  13.6  153 11.3 9.6  150 15.6 10.3  167 16.3 8.7  102 11.7 9.9  227 22.9 9.6  134 14.0
250628150238  13.3  138 10.4 9.6  137 14.3 10.2  152 14.9 8.7   94 10.7 9.8  208 21.1 9.5  122 12.7
250628150538  12.9  122 9.4 9.5  123 12.9 10.1  136 13.4 8.7   85 9.8 9.8  190 19.4 9.5  109 11.5
250628150838  12.8  113 8.9 9.5  115 12.1 10.1  126 12.5 8.7   79 9.1 9.7  172 17.7 9.5  103 10.9
250628151138  12.8  116 9.0 9.5  118 12.4 10.1  128 12.7 8.7   79 9.1 9.7  165 17.0 9.5  105 11.1
250628151438  13.3  139 10.4 9.6  138 14.4 10.2  152 14.8 8.7   89 10.3 9.8  193 19.7 9.6  123 12.8
250628151738  13.7  159 11.6 9.6  155 16.1 10.3  173 16.8 8.7  100 11.5 9.9  225 22.8 9.6  137 14.3
250628152038  13.9  171 12.3 9.7  164 17.0 10.3  186 18.0 8.8  108 12.4 10.0  254 25.5 9.6  146 15.2
250628152338  13.9  172 12.3 9.7  166 17.2 10.3  186 18.0 8.8  109 12.5 10.0  251 25.2 9.6  148 15.3
250628152638  14.1  184 13.0 9.7  176 18.1 10.4  197 19.0 8.8  115 13.1 10.0  264 26.4 9.7  156 16.2
250628152938  14.2  187 13.2 9.7  179 18.4 10.4  197 19.0 8.8  116 13.3 10.0  260 26.1 9.7  159 16.4
250628153238  14.0  177 12.7 9.7  171 17.6 10.3  189 18.3 8.8  112 12.8 10.0  251 25.2 9.7  152 15.8
250628153538  13.5  148 11.0 9.6  146 15.2 10.2  161 15.8 8.7   99 11.3 9.9  219 22.2 9.6  130 13.6
250628153838  13.0  124 9.5 9.5  125 13.1 10.1  137 13.5 8.7   85 9.8 9.8  184 18.9 9.5  111 11.7
250628154138  12.9  120 9.3 9.5  122 12.8 10.1  132 13.0 8.7   82 9.4 9.7  174 17.9 9.5  108 11.4
250628154438  13.1  128 9.8 9.5  128 13.4 10.2  141 13.9 8.7   87 10.0 9.8  193 19.7 9.5  114 12.0
250628154738  12.7  109 8.6 9.5  112 11.8 10.1  124 12.2 8.6   77 8.9 9.7  175 17.9 9.5  100 10.5
250628155038  12.4   96 7.8 9.4  100 10.6 10.0  111 11.0 8.6   70 8.1 9.7  154 16.0 9.4   89 9.4