import pandas as pd

df = pd.read_csv("fashion_trend_data.csv")
print(df)

        Trend_ID  Trend_Name       Category  Subcategory  \
0       A9634DC8      Luxury         Babies     Clothing   
1       68FF3CBC      Casual            Men     Clothing   
2       79547BF3         Y2K           Kids  Accessories   
3       8C6B7EC7  Streetwear            Men     Clothing   
4       5EEF6F4D      Formal           Kids     Footwear   
...          ...         ...            ...          ...   
199995  8EAE02EE         Y2K   Teenage Boys  Accessories   
199996  E0CB552D      Luxury           Kids  Accessories   
199997  E3EB229F         Y2K  Teenage Girls  Accessories   
199998  908FD41F         Y2K  Teenage Girls     Clothing   
199999  EB922A51      Casual         Babies     Footwear   

                                   Description  Start_Date    End_Date  \
0              Luxury trend in Babies Clothing  2024-03-22  2024-11-07   
1                 Casual trend in Men Clothing  2024-12-03  2025-01-04   
2                Y2K trend in Kids Accessories  2023-02-05  2026-02-08   
3             Streetwear trend in Men Clothing  2024-02-05  2026-07-05   
4                Formal trend in Kids Footwear  2024-05-30  2025-08-13   
...                                        ...         ...         ...   
199995   Y2K trend in Teenage Boys Accessories  2023-09-25  2025-07-08   
199996        Luxury trend in Kids Accessories  2022-11-05  2025-12-15   
199997  Y2K trend in Teenage Girls Accessories  2022-06-27  2024-07-04   
199998     Y2K trend in Teenage Girls Clothing  2024-09-15  2025-09-08   
199999         Casual trend in Babies Footwear  2023-06-18  2025-05-09   

        Popularity_Score                    Keywords             Influencer  \
0                     71        Luxury, Pink, Floral            Debra Smith   
1                     70      Casual, Blue, Abstract       Mr. Joshua Smith   
2                     69        Y2K, White, Abstract          Thomas Wagner   
3                     61  Streetwear, Green, Graphic  Jacqueline Strickland   
4                     91      Formal, White, Graphic          Edward Garcia   
...                  ...                         ...                    ...   
199995                63       Y2K, Yellow, Abstract        Heather Russell   
199996                76      Luxury, White, Striped          Alex Mcdonald   
199997                83        Y2K, Yellow, Striped       Cynthia Stephens   
199998                54         Y2K, Black, Striped           Jeffrey Cruz   
199999                60    Casual, Black, Checkered          Micheal Doyle   

        ...  Google_Search_Keyword Platform_Hashtag  Social_Media_Likes  \
0       ...                 luxury          #Luxury              312991   
1       ...                 casual          #Casual              107868   
2       ...                    y2k             #Y2K               72055   
3       ...             streetwear      #Streetwear              191921   
4       ...                 formal          #Formal              311960   
...     ...                    ...              ...                 ...   
199995  ...                    y2k             #Y2K              216979   
199996  ...                 luxury          #Luxury              175321   
199997  ...                    y2k             #Y2K               42172   
199998  ...                    y2k             #Y2K              290607   
199999  ...                 casual          #Casual              395113   

        Shares  Comments  Sentiment_Score    Runway_Designer  \
0        25680      2407             0.63      Jeffrey Brown   
1         1878     13804             0.51    Marvin Castillo   
2        25038      7286             0.72     Janice Perkins   
3        37138     19720             0.55    Charles Vazquez   
4        47785      6986             0.96    Kathleen Martin   
...        ...       ...              ...                ...   
199995   14317      9714             0.75      Katrina Walls   
199996   25048     19298             0.86    Stephanie Brown   
199997    1834      9995             0.52  Mr. Samuel Romero   
199998    6339      9906             0.62   William Williams   
199999   10559      6201             0.79    Brittany Chavez   

              Fashion_Show      City      Featured_Items  
0       Milan Fashion Week  New York       Clothing, Red  
1       Paris Fashion Week    London     Clothing, Black  
2       Paris Fashion Week    London     Clothing, Green  
3       Paris Fashion Week  New York    Footwear, Yellow  
4       Milan Fashion Week     Paris    Clothing, Yellow  
...                    ...       ...                 ...  
199995  Milan Fashion Week     Paris  Accessories, White  
199996  Paris Fashion Week    London   Accessories, Blue  
199997  Paris Fashion Week    London      Footwear, Pink  
199998  Paris Fashion Week    London   Accessories, Pink  
199999  Paris Fashion Week    London      Clothing, Pink  

[200000 rows x 41 columns]

df

	Trend_ID	Trend_Name	Category	Subcategory	Description	Start_Date	End_Date	Popularity_Score	Keywords	Influencer	...	Google_Search_Keyword	Platform_Hashtag	Social_Media_Likes	Shares	Comments	Sentiment_Score	Runway_Designer	Fashion_Show	City	Featured_Items
0	A9634DC8	Luxury	Babies	Clothing	Luxury trend in Babies Clothing	2024-03-22	2024-11-07	71	Luxury, Pink, Floral	Debra Smith	...	luxury	#Luxury	312991	25680	2407	0.63	Jeffrey Brown	Milan Fashion Week	New York	Clothing, Red
1	68FF3CBC	Casual	Men	Clothing	Casual trend in Men Clothing	2024-12-03	2025-01-04	70	Casual, Blue, Abstract	Mr. Joshua Smith	...	casual	#Casual	107868	1878	13804	0.51	Marvin Castillo	Paris Fashion Week	London	Clothing, Black
2	79547BF3	Y2K	Kids	Accessories	Y2K trend in Kids Accessories	2023-02-05	2026-02-08	69	Y2K, White, Abstract	Thomas Wagner	...	y2k	#Y2K	72055	25038	7286	0.72	Janice Perkins	Paris Fashion Week	London	Clothing, Green
3	8C6B7EC7	Streetwear	Men	Clothing	Streetwear trend in Men Clothing	2024-02-05	2026-07-05	61	Streetwear, Green, Graphic	Jacqueline Strickland	...	streetwear	#Streetwear	191921	37138	19720	0.55	Charles Vazquez	Paris Fashion Week	New York	Footwear, Yellow
4	5EEF6F4D	Formal	Kids	Footwear	Formal trend in Kids Footwear	2024-05-30	2025-08-13	91	Formal, White, Graphic	Edward Garcia	...	formal	#Formal	311960	47785	6986	0.96	Kathleen Martin	Milan Fashion Week	Paris	Clothing, Yellow
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
199995	8EAE02EE	Y2K	Teenage Boys	Accessories	Y2K trend in Teenage Boys Accessories	2023-09-25	2025-07-08	63	Y2K, Yellow, Abstract	Heather Russell	...	y2k	#Y2K	216979	14317	9714	0.75	Katrina Walls	Milan Fashion Week	Paris	Accessories, White
199996	E0CB552D	Luxury	Kids	Accessories	Luxury trend in Kids Accessories	2022-11-05	2025-12-15	76	Luxury, White, Striped	Alex Mcdonald	...	luxury	#Luxury	175321	25048	19298	0.86	Stephanie Brown	Paris Fashion Week	London	Accessories, Blue
199997	E3EB229F	Y2K	Teenage Girls	Accessories	Y2K trend in Teenage Girls Accessories	2022-06-27	2024-07-04	83	Y2K, Yellow, Striped	Cynthia Stephens	...	y2k	#Y2K	42172	1834	9995	0.52	Mr. Samuel Romero	Paris Fashion Week	London	Footwear, Pink
199998	908FD41F	Y2K	Teenage Girls	Clothing	Y2K trend in Teenage Girls Clothing	2024-09-15	2025-09-08	54	Y2K, Black, Striped	Jeffrey Cruz	...	y2k	#Y2K	290607	6339	9906	0.62	William Williams	Paris Fashion Week	London	Accessories, Pink
199999	EB922A51	Casual	Babies	Footwear	Casual trend in Babies Footwear	2023-06-18	2025-05-09	60	Casual, Black, Checkered	Micheal Doyle	...	casual	#Casual	395113	10559	6201	0.79	Brittany Chavez	Paris Fashion Week	London	Clothing, Pink

200000 rows × 41 columns

import pandas as pd
import plotly.express as px
# Convert date columns to datetime format
df['Start_Date'] = pd.to_datetime(df['Start_Date'])
df['End_Date'] = pd.to_datetime(df['End_Date'])

### 1️⃣ Trending Fashion Categories Over Time ###
category_trends = df.groupby(['Start_Date', 'Category']).size().reset_index(name='Count')
fig1 = px.line(category_trends, x="Start_Date", y="Count", color="Category",
               title="🔥 Trending Fashion Categories Over Time")
fig1.show()

### 2️⃣ Influencer Impact on Fashion Trends ###
fig2 = px.scatter(df, x="Followers", y="Engagement_Rate", color="Platform",
                  size="Avg_Likes", hover_data=["Influencer"],
                  title="📣 Influencer Impact on Fashion Trends")
fig2.show()

### 3️⃣ Revenue vs. Units Sold per Brand ###
brand_performance = df.groupby("Brand").agg({"Revenue": "sum", "Units_Sold": "sum"}).reset_index()
fig3 = px.scatter(brand_performance, x="Units_Sold", y="Revenue", size="Revenue", color="Brand",
                  title="💰 Revenue vs. Units Sold per Brand", hover_name="Brand")
fig3.show()

### 4️⃣ Global Fashion Trends Map ###
country_trends = df.groupby("Region").agg({"Search_Volume": "sum"}).reset_index()
fig4 = px.choropleth(country_trends, locations="Region", locationmode="country names",
                     color="Search_Volume", title="🌍 Global Fashion Trends Popularity",
                     color_continuous_scale="blues")
fig4.show()

### 5️⃣ Social Media Engagement Trends ###
engagement_metrics = df.groupby("Trend_Name").agg({"Avg_Likes": "sum", "Shares": "sum", "Comments": "sum"}).reset_index()
fig5 = px.bar(engagement_metrics, x="Trend_Name", y=["Avg_Likes", "Shares", "Comments"],
              title="📈 Social Media Engagement Trends", barmode="group")
fig5.show()

import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from collections import Counter
from wordcloud import WordCloud
import matplotlib.pyplot as plt


# Convert date columns to datetime format
#df['Start_Date'] = pd.to_datetime(df['Start_Date'])
#df['End_Date'] = pd.to_datetime(df['End_Date'])

### 1️⃣ Fashion Trends Popularity Over Time (Animated Bar Chart) ###
trend_time = df.groupby(['Start_Date', 'Trend_Name']).size().reset_index(name='Count')
fig1 = px.bar(trend_time, x="Trend_Name", y="Count", animation_frame="Start_Date",
              color="Trend_Name", title="🔥 Fashion Trends Popularity Over Time")
fig1.show()

### 2️⃣ Top Fashion Brands by Revenue & Units Sold (Sunburst Chart) ###
brand_data = df.groupby(["Brand", "Category"]).agg({"Revenue": "sum", "Units_Sold": "sum"}).reset_index()
fig2 = px.sunburst(brand_data, path=["Brand", "Category"], values="Revenue",
                   title="💰 Top Fashion Brands by Revenue & Units Sold", color="Revenue")
fig2.show()

### 4️⃣ Correlation Between Price and Popularity (Heatmap) ###
price_vs_popularity = df[["Price", "Popularity_Score"]].corr()
fig4 = px.imshow(price_vs_popularity, text_auto=True, title="🔥 Correlation Between Price & Popularity")
fig4.show()

### 5️⃣ Most Popular Fashion Colors (Word Cloud) ###
color_counts = Counter(df["Color"])
wordcloud = WordCloud(width=800, height=400, background_color="white").generate_from_frequencies(color_counts)
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis("off")
plt.title("🎨 Most Popular Fashion Colors")
plt.show()

! pip install dash

! pip install pandas numpy plotly dash prophet

# @title  Final Code

import pandas as pd
import plotly.express as px
import dash
from dash import dcc, html
from dash.dependencies import Input, Output
from prophet import Prophet


# Load dataset
#df = pd.read_csv("fashion_trend_data.csv")
df['Start_Date'] = pd.to_datetime(df['Start_Date'])

# Initialize Dash app
app = dash.Dash(__name__)

# Layout
app.layout = html.Div([
    html.H1("🔮 Fashion Trend Forecasting Dashboard", style={'textAlign': 'center'}),

    # Dropdowns for filtering
    html.Div([
        html.Label("Select Trend Feature:"),
        dcc.Dropdown(
            id="feature-dropdown",
            options=[
                {"label": "Trend Name", "value": "Trend_Name"},
                {"label": "Category", "value": "Category"},
                {"label": "Subcategory", "value": "Subcategory"},
                {"label": "Product Name", "value": "Product_Name"},
                {"label": "Brand", "value": "Brand"},
                {"label": "Material", "value": "Material"},
                {"label": "Color", "value": "Color"},
                {"label": "Region", "value": "Region"},
                {"label": "City", "value": "City"}
            ],
            value="Trend_Name",
            clearable=False
        ),
        html.Label("Select a Value:"),
        dcc.Dropdown(id="value-dropdown", clearable=False),
    ]),

    # Graph for Forecast
    dcc.Graph(id="forecast-graph"),

    # Additional Insights
    html.Div([
        html.H3("📊 Additional Insights", style={'textAlign': 'center'}),
        dcc.Graph(id="bar-chart"),
        dcc.Graph(id="pie-chart"),
    ])
])

# Update second dropdown based on feature selection
@app.callback(
    Output("value-dropdown", "options"),
    Input("feature-dropdown", "value")
)
def update_value_dropdown(selected_feature):
    options = [{"label": val, "value": val} for val in df[selected_feature].unique()]
    return options

# Forecasting Callback
@app.callback(
    Output("forecast-graph", "figure"),
    Input("feature-dropdown", "value"),
    Input("value-dropdown", "value")
)
def forecast_trend(selected_feature, selected_value):
    if not selected_value:
        return px.line(title="Select a value to generate forecast")

    # Prepare data
    trend_data = df[df[selected_feature] == selected_value].groupby("Start_Date").size().reset_index(name="Count")
    trend_data.rename(columns={"Start_Date": "ds", "Count": "y"}, inplace=True)

    # Train Prophet model
    model = Prophet(yearly_seasonality=True, daily_seasonality=False)
    model.fit(trend_data)

    # Future prediction
    future = model.make_future_dataframe(periods=365, freq="D")
    forecast = model.predict(future)

    # Visualization
    fig = px.line()
    fig.add_scatter(x=trend_data["ds"], y=trend_data["y"], mode="lines", name="Actual Trend")
    fig.add_scatter(x=forecast["ds"], y=forecast["yhat"], mode="lines", name="Forecasted Trend", line=dict(dash="dot"))

    fig.update_layout(title=f"📈 Forecast for {selected_value}", xaxis_title="Date", yaxis_title="Popularity Score")
    return fig

# Additional Insights: Bar Chart (Popularity Score)
@app.callback(
    Output("bar-chart", "figure"),
    Input("feature-dropdown", "value"),
    Input("value-dropdown", "value")
)
def generate_bar_chart(selected_feature, selected_value):
    if not selected_value:
        return px.bar(title="Select a value for insights")

    trend_data = df[df[selected_feature] == selected_value]
    fig = px.bar(trend_data, x="Brand", y="Popularity_Score", color="Brand", title="🔥 Brand Popularity Score")
    return fig

# Additional Insights: Pie Chart (Category Distribution)
@app.callback(
    Output("pie-chart", "figure"),
    Input("feature-dropdown", "value"),
    Input("value-dropdown", "value")
)
def generate_pie_chart(selected_feature, selected_value):
    if not selected_value:
        return px.pie(title="Select a value for insights")

    trend_data = df[df[selected_feature] == selected_value]
    fig = px.pie(trend_data, names="Category", title="👗 Fashion Category Distribution")
    return fig

# Run the Dash app
if __name__ == "__main__":
    app.run(debug=True)