Delete app.py

app.py

@@ -1,137 +0,0 @@
-import pandas as pd
-import numpy as np
-import gradio as gr
-import plotly.express as px
-import random
-import warnings
-
-warnings.filterwarnings("ignore")
-random.seed(2025)
-np.random.seed(2025)
-
-def process_data(file):
-    df = pd.read_csv(file.name)
-
-    df.columns = df.columns.str.strip().str.lower()
-    df = df.drop_duplicates()
-
-    for col in ["delivery_time_hours", "expected_time_hours"]:
-        df[col] = df[col].astype(str).str.split(".").str[-1]
-        df[col] = pd.to_numeric(df[col], errors="coerce")
-
-    numeric_cols = [
-        "distance_km","package_weight_kg",
-        "delivery_time_hours","expected_time_hours",
-        "delivery_rating","delivery_cost"
-    ]
-
-    for col in numeric_cols:
-        df[col] = pd.to_numeric(df[col], errors="coerce")
-        df[col] = df[col].fillna(df[col].median())
-
-    categorical_cols = [
-        "delivery_partner","package_type","vehicle_type",
-        "delivery_mode","region","weather_condition",
-        "delayed","delivery_status"
-    ]
-
-    for col in categorical_cols:
-        df[col] = df[col].astype(str).fillna(df[col].mode()[0])
-
-    df["expected_time_hours"] = df["distance_km"] / 45
-    df["delivery_time_hours"] = df["expected_time_hours"] * np.random.uniform(0.9, 1.2, len(df))
-
-    df["delay_hours"] = df["delivery_time_hours"] - df["expected_time_hours"]
-
-    df["delay_score"] = df["delay_hours"].apply(
-        lambda x: 5 if x <= 0 else 4 if x <= 2 else 3 if x <= 5 else 2 if x <= 8 else 1
-    )
-
-    df["performance_label"] = df["delay_score"].map({
-        5:"Excellent",4:"Good",3:"Average",2:"Poor",1:"Critical"
-    })
-
-    return df
-
-def kpi_section(df):
-    avg_delay = round(df["delay_hours"].mean(),2)
-    delay_rate = round((df["delay_hours"] > 0).mean()*100,2)
-    score = round(df["delay_score"].mean(),2)
-
-    return f"""### KPI Overview
-
-- Average Delay: {avg_delay} hours  
-- Delay Rate: {delay_rate}%  
-- Performance Score: {score}  
-"""
-
-def quantitative_section(df):
-    fig1 = px.bar(df.groupby("vehicle_type")["delay_hours"].mean().reset_index(),
-                  x="vehicle_type", y="delay_hours", title="Delay by Vehicle Type")
-
-    fig2 = px.bar(df.groupby("weather_condition")["delay_hours"].mean().reset_index(),
-                  x="weather_condition", y="delay_hours", title="Delay by Weather")
-
-    return fig1, fig2
-
-def qualitative_section(df):
-    worst_vehicle = df.groupby("vehicle_type")["delay_hours"].mean().idxmax()
-    worst_weather = df.groupby("weather_condition")["delay_hours"].mean().idxmax()
-
-    return f"""### Qualitative Insights
-
-The analysis shows that **{worst_vehicle}** vehicles are associated with the highest delays.
-
-Additionally, **{worst_weather}** conditions significantly increase delivery variability.
-
-This suggests both internal and external factors drive delays.
-"""
-
-def recommendation_section(df):
-    best_vehicle = df.groupby("vehicle_type")["delay_score"].mean().idxmax()
-    worst_vehicle = df.groupby("vehicle_type")["delay_score"].mean().idxmin()
-
-    return f"""### AI Management Recommendations
-
-- Use more **{best_vehicle}** vehicles  
-- Reduce reliance on **{worst_vehicle}**  
-- Optimize routes under difficult weather conditions  
-- Improve weakest operational segments  
-"""
-
-def run_dashboard(file):
-    df = process_data(file)
-
-    kpi = kpi_section(df)
-    fig1, fig2 = quantitative_section(df)
-    qual = qualitative_section(df)
-    rec = recommendation_section(df)
-
-    return kpi, fig1, fig2, qual, rec
-
-with gr.Blocks() as demo:
-    gr.Markdown("# AI Delivery Performance Dashboard")
-
-    file_input = gr.File(label="Upload CSV")
-    btn = gr.Button("Generate Dashboard")
-
-    with gr.Tab("1. KPI Overview"):
-        kpi_out = gr.Markdown()
-
-    with gr.Tab("2. Quantitative Analysis"):
-        chart1 = gr.Plot()
-        chart2 = gr.Plot()
-
-    with gr.Tab("3. Qualitative Analysis"):
-        qual_out = gr.Markdown()
-
-    with gr.Tab("4. AI Management Recommendations"):
-        rec_out = gr.Markdown()
-
-    btn.click(
-        run_dashboard,
-        inputs=file_input,
-        outputs=[kpi_out, chart1, chart2, qual_out, rec_out]
-    )
-
-demo.launch()