Graph plots weirdly, does anyone know why?

📊 Plotly Python
1 
import dash
from dash import html, dcc, Input, Output, callback
import pandas as pd
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Register the page (if using multi-page app)
dash.register_page(__name__, path="/comparison_for_dataset_2", title="Dataset 2 Comparison", name="Dataset 2 Comparison")

df = pd.read_csv(file_path1)
df1 = pd.read_csv(file_path2)

cols = df.columns
cols1 = df1.columns

# Data cleaning and merging function
def process_data(df, df1):
    df_cleaned = df.dropna()
    df1_cleaned = df1.dropna()

    df1_cleaned_2 = df1_cleaned.drop(cols1[0], axis=1)
    # Merge the cleaned DataFrames
    conc_df = pd.concat([df_cleaned, df1_cleaned_2], axis=1)
    cleaned_merged_df = conc_df.fillna(0)

    cleaned_merged_df_min_max_scaled = cleaned_merged_df.copy()
    for col in cleaned_merged_df_min_max_scaled.columns:
        if col != cols1[0] and cleaned_merged_df_min_max_scaled[col].max() != cleaned_merged_df_min_max_scaled[col].min():
            cleaned_merged_df_min_max_scaled[col] = (
                (cleaned_merged_df_min_max_scaled[col] - cleaned_merged_df_min_max_scaled[col].min()) /
                (cleaned_merged_df_min_max_scaled[col].max() - cleaned_merged_df_min_max_scaled[col].min())
            )
    return cleaned_merged_df_min_max_scaled

# Apply data cleaning and merging
merged_df = process_data(df, df1)

# Page Layout
layout = html.Div([
    html.H2("Dynamic Graph Visualization with Double Y-axis", style={'textAlign': 'center'}),
    html.Label("Select Graph Type:"),
    dcc.Dropdown(
        id='graph-type',
        options=[
            {'label': 'Line Graph', 'value': 'line'},
            {'label': 'Scatter Plot', 'value': 'scatter'},
            {'label': 'Bar Chart', 'value': 'bar'},
            {'label': 'Histogram', 'value': 'histogram'}
        ],
        value='line',
        style={'width': '300px'}
    ),
    html.Br(),
    html.Label("Select X-axis:"),
    dcc.Dropdown(
        id='x-axis',
        options=[{'label': col, 'value': col} for col in merged_df.columns],
        value=merged_df.columns[0],
        style={'width': '300px'}
    ),
    html.Br(),
    html.Label("Select Y-axis for Primary Axis (can select multiple):"),
    dcc.Dropdown(
        id='primary-y-axis',
        options=[{'label': col, 'value': col} for col in merged_df.columns if col != 'Time'],
        value=[merged_df.columns[1]],
        multi=True,
        style={'width': '300px'}
    ),
    html.Br(),
    html.Label("Select Y-axis for Secondary Axis (can select multiple):"),
    dcc.Dropdown(
        id='secondary-y-axis',
        options=[{'label': col, 'value': col} for col in merged_df.columns if col != 'Time'],
        value=[],
        multi=True,
        style={'width': '300px'}
    ),
    html.Br(),
    dcc.Graph(id='graph-output', style={'height': '800px'})
])

# Callback for dynamic graph updates
@callback(
    Output('graph-output', 'figure'),
    [Input('graph-type', 'value'),
     Input('x-axis', 'value'),
     Input('primary-y-axis', 'value'),
     Input('secondary-y-axis', 'value')]
)
def update_graph(graph_type, x_col, primary_y_cols, secondary_y_cols):
    """
    Update the graph based on selected options for graph type, X-axis, primary Y-axis, and secondary Y-axis.
    """
    # Ensure selected columns exist in the merged DataFrame
    if x_col not in merged_df.columns:
        return go.Figure()

    for y_col in primary_y_cols + secondary_y_cols:
        if y_col not in merged_df.columns:
            return go.Figure()

    # Drop rows with NaN in selected columns
    cleaned_df = merged_df.dropna(subset=[x_col] + primary_y_cols + secondary_y_cols)

    # Create a figure with secondary Y-axis
    fig = make_subplots(specs=[[{"secondary_y": True}]])

    # Add traces for primary Y-axis
    for col in primary_y_cols:
        if graph_type == 'line':
            fig.add_trace(go.Scatter(x=cleaned_df[x_col], y=cleaned_df[col], name=col, mode='lines'), secondary_y=False)
        elif graph_type == 'scatter':
            fig.add_trace(go.Scatter(x=cleaned_df[x_col], y=cleaned_df[col], name=col, mode='markers'), secondary_y=False)
        elif graph_type == 'bar':
            fig.add_trace(go.Bar(x=cleaned_df[x_col], y=cleaned_df[col], name=col), secondary_y=False)
        elif graph_type == 'histogram':
            fig.add_trace(go.Histogram(x=cleaned_df[x_col], y=cleaned_df[col], name=col), secondary_y=False)

    # Add traces for secondary Y-axis
    for col in secondary_y_cols:
        if graph_type == 'line':
            fig.add_trace(go.Scatter(x=cleaned_df[x_col], y=cleaned_df[col], name=col, mode='lines', line=dict(dash='dot')), secondary_y=True)
        elif graph_type == 'scatter':
            fig.add_trace(go.Scatter(x=cleaned_df[x_col], y=cleaned_df[col], name=col, mode='markers'), secondary_y=True)
        elif graph_type == 'bar':
            fig.add_trace(go.Bar(x=cleaned_df[x_col], y=cleaned_df[col], name=col, marker=dict(opacity=0.5)), secondary_y=True)
        elif graph_type == 'histogram':
            fig.add_trace(go.Histogram(x=cleaned_df[x_col], y=cleaned_df[col], name=col), secondary_y=True)

    # Update layout
    fig.update_layout(
        title=f"{graph_type.capitalize()} with {x_col}, Primary Y-axis: {', '.join(primary_y_cols)}, Secondary Y-axis: {', '.join(secondary_y_cols)}",
        xaxis_title=x_col,
        yaxis_title=", ".join(primary_y_cols),
        yaxis2_title=", ".join(secondary_y_cols),
        legend_title="Legend",
    )

    return fig

The datasets are omitted for my own protection. But they are there. Every time I run the app, when I go to this page, the graph plots straight lines going straight up. When I run it in my debugging code, the graph is just fine. Is it something with the normalisation? If anyone can help, it would be greatly appreciated, thanks!