|
|
import os |
|
|
import sys |
|
|
from dotenv import load_dotenv |
|
|
import gradio as gr |
|
|
from typing import Optional, Dict, List, Union |
|
|
import logging |
|
|
|
|
|
|
|
|
CUSTOM_CSS = """ |
|
|
.footer { |
|
|
text-align: center !important; |
|
|
padding: 20px !important; |
|
|
margin-top: 40px !important; |
|
|
border-top: 1px solid #404040 !important; |
|
|
color: #89CFF0 !important; |
|
|
font-size: 1.1em !important; |
|
|
} |
|
|
|
|
|
.gradio-container { |
|
|
max-width: 1200px !important; |
|
|
margin: auto !important; |
|
|
padding: 20px !important; |
|
|
background-color: #1a1a1a !important; |
|
|
color: #ffffff !important; |
|
|
} |
|
|
|
|
|
.main-header { |
|
|
background: linear-gradient(135deg, #1e3c72 0%, #2a5298 100%) !important; |
|
|
color: white !important; |
|
|
padding: 30px !important; |
|
|
border-radius: 15px !important; |
|
|
margin-bottom: 30px !important; |
|
|
text-align: center !important; |
|
|
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2) !important; |
|
|
} |
|
|
|
|
|
.app-title { |
|
|
font-size: 2.5em !important; |
|
|
font-weight: bold !important; |
|
|
margin-bottom: 10px !important; |
|
|
background: linear-gradient(90deg, #ffffff, #3498DB) !important; |
|
|
-webkit-background-clip: text !important; |
|
|
-webkit-text-fill-color: transparent !important; |
|
|
text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3) !important; |
|
|
} |
|
|
|
|
|
.app-subtitle { |
|
|
font-size: 1.3em !important; |
|
|
color: #89CFF0 !important; |
|
|
margin-bottom: 15px !important; |
|
|
font-weight: 500 !important; |
|
|
} |
|
|
|
|
|
.app-description { |
|
|
font-size: 1.1em !important; |
|
|
color: #B0C4DE !important; |
|
|
margin-bottom: 20px !important; |
|
|
line-height: 1.5 !important; |
|
|
} |
|
|
|
|
|
.gr-checkbox-group { |
|
|
background: #363636 !important; |
|
|
padding: 15px !important; |
|
|
border-radius: 10px !important; |
|
|
margin: 10px 0 !important; |
|
|
} |
|
|
|
|
|
.gr-slider { |
|
|
margin-top: 10px !important; |
|
|
} |
|
|
|
|
|
.status-message { |
|
|
margin-top: 10px !important; |
|
|
padding: 8px !important; |
|
|
border-radius: 4px !important; |
|
|
background-color: #2d2d2d !important; |
|
|
} |
|
|
|
|
|
.result-box { |
|
|
background: #363636 !important; |
|
|
border: 1px solid #404040 !important; |
|
|
border-radius: 10px !important; |
|
|
padding: 20px !important; |
|
|
margin-top: 15px !important; |
|
|
color: #ffffff !important; |
|
|
} |
|
|
|
|
|
.chart-container { |
|
|
background: #2d2d2d !important; |
|
|
padding: 20px !important; |
|
|
border-radius: 10px !important; |
|
|
box-shadow: 0 2px 4px rgba(0,0,0,0.2) !important; |
|
|
color: #ffffff !important; |
|
|
} |
|
|
|
|
|
.action-button { |
|
|
background: #3498DB !important; |
|
|
color: white !important; |
|
|
border: none !important; |
|
|
padding: 10px 20px !important; |
|
|
border-radius: 5px !important; |
|
|
cursor: pointer !important; |
|
|
transition: all 0.3s ease !important; |
|
|
} |
|
|
|
|
|
.action-button:hover { |
|
|
background: #2980B9 !important; |
|
|
transform: translateY(-2px) !important; |
|
|
box-shadow: 0 4px 8px rgba(0,0,0,0.2) !important; |
|
|
} |
|
|
""" |
|
|
|
|
|
|
|
|
logging.basicConfig( |
|
|
level=logging.INFO, |
|
|
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' |
|
|
) |
|
|
logger = logging.getLogger(__name__) |
|
|
|
|
|
|
|
|
load_dotenv() |
|
|
|
|
|
|
|
|
MAX_FILE_SIZE = 50 * 1024 * 1024 |
|
|
ALLOWED_EXTENSIONS = {'.xlsx', '.xls', '.csv'} |
|
|
import pandas as pd |
|
|
import google.generativeai as genai |
|
|
import joblib |
|
|
from reportlab.lib import colors |
|
|
from reportlab.lib.pagesizes import letter |
|
|
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, Image |
|
|
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle |
|
|
import plotly.express as px |
|
|
import plotly.graph_objects as go |
|
|
import tempfile |
|
|
from datetime import datetime |
|
|
import numpy as np |
|
|
from xgboost import XGBRegressor |
|
|
|
|
|
|
|
|
GEMINI_API_KEY = os.getenv("gemini_api") |
|
|
|
|
|
genai.configure(api_key=GEMINI_API_KEY) |
|
|
generation_config = { |
|
|
"temperature": 1, |
|
|
"top_p": 0.95, |
|
|
"top_k": 64, |
|
|
"max_output_tokens": 8192, |
|
|
"response_mime_type": "text/plain", |
|
|
} |
|
|
|
|
|
model = genai.GenerativeModel( |
|
|
model_name="gemini-2.0-pro-exp-02-05", |
|
|
generation_config=generation_config, |
|
|
) |
|
|
|
|
|
chat_model = genai.GenerativeModel('"gemini-2.0-pro-exp-02-05"') |
|
|
|
|
|
class SupplyChainState: |
|
|
def __init__(self): |
|
|
self.sales_df = None |
|
|
self.supplier_df = None |
|
|
self.text_data = None |
|
|
self.chat_history = [] |
|
|
self.analysis_results = {} |
|
|
self.freight_predictions = [] |
|
|
|
|
|
try: |
|
|
self.freight_model = create_initial_model() |
|
|
except Exception as e: |
|
|
print(f"Warning: Could not create freight prediction model: {e}") |
|
|
self.freight_model = None |
|
|
|
|
|
def create_initial_model(): |
|
|
n_samples = 1000 |
|
|
np.random.seed(42) |
|
|
|
|
|
data = { |
|
|
'weight (kilograms)': np.random.uniform(100, 10000, n_samples), |
|
|
'line item value': np.random.uniform(1000, 1000000, n_samples), |
|
|
'cost per kilogram': np.random.uniform(1, 500, n_samples), |
|
|
'shipment mode_Air Charter_weight': np.zeros(n_samples), |
|
|
'shipment mode_Ocean_weight': np.zeros(n_samples), |
|
|
'shipment mode_Truck_weight': np.zeros(n_samples), |
|
|
'shipment mode_Air Charter_line_item_value': np.zeros(n_samples), |
|
|
'shipment mode_Ocean_line_item_value': np.zeros(n_samples), |
|
|
'shipment mode_Truck_line_item_value': np.zeros(n_samples) |
|
|
} |
|
|
|
|
|
modes = ['Air', 'Ocean', 'Truck'] |
|
|
for i in range(n_samples): |
|
|
mode = np.random.choice(modes) |
|
|
if mode == 'Air': |
|
|
data['shipment mode_Air Charter_weight'][i] = data['weight (kilograms)'][i] |
|
|
data['shipment mode_Air Charter_line_item_value'][i] = data['line item value'][i] |
|
|
elif mode == 'Ocean': |
|
|
data['shipment mode_Ocean_weight'][i] = data['weight (kilograms)'][i] |
|
|
data['shipment mode_Ocean_line_item_value'][i] = data['line item value'][i] |
|
|
else: |
|
|
data['shipment mode_Truck_weight'][i] = data['weight (kilograms)'][i] |
|
|
data['shipment mode_Truck_line_item_value'][i] = data['line item value'][i] |
|
|
|
|
|
df = pd.DataFrame(data) |
|
|
base_cost = (df['weight (kilograms)'] * df['cost per kilogram'] * 0.8 + |
|
|
df['line item value'] * 0.02) |
|
|
|
|
|
air_multiplier = 1.5 |
|
|
ocean_multiplier = 0.8 |
|
|
truck_multiplier = 1.0 |
|
|
|
|
|
freight_cost = ( |
|
|
base_cost * (air_multiplier * (df['shipment mode_Air Charter_weight'] > 0) + |
|
|
ocean_multiplier * (df['shipment mode_Ocean_weight'] > 0) + |
|
|
truck_multiplier * (df['shipment mode_Truck_weight'] > 0)) |
|
|
) |
|
|
|
|
|
freight_cost = freight_cost + np.random.normal(0, freight_cost * 0.1) |
|
|
|
|
|
model = XGBRegressor(n_estimators=100, learning_rate=0.1, max_depth=5, random_state=42) |
|
|
model.fit(df, freight_cost) |
|
|
|
|
|
return model |
|
|
|
|
|
def process_uploaded_data(state, sales_file, supplier_file, text_data): |
|
|
try: |
|
|
if sales_file is not None: |
|
|
file_ext = os.path.splitext(sales_file.name)[1].lower() |
|
|
if file_ext not in ['.xlsx', '.xls', '.csv']: |
|
|
return '❌ Error: Sales data must be in Excel (.xlsx, .xls) or CSV format' |
|
|
|
|
|
try: |
|
|
if file_ext == '.csv': |
|
|
state.sales_df = pd.read_csv(sales_file.name) |
|
|
else: |
|
|
state.sales_df = pd.read_excel(sales_file.name) |
|
|
except Exception as e: |
|
|
return f'❌ Error reading sales data: {str(e)}' |
|
|
|
|
|
if supplier_file is not None: |
|
|
file_ext = os.path.splitext(supplier_file.name)[1].lower() |
|
|
if file_ext not in ['.xlsx', '.xls', '.csv']: |
|
|
return '❌ Error: Supplier data must be in Excel (.xlsx, .xls) or CSV format' |
|
|
|
|
|
try: |
|
|
if file_ext == '.csv': |
|
|
state.supplier_df = pd.read_csv(supplier_file.name) |
|
|
else: |
|
|
state.supplier_df = pd.read_excel(supplier_file.name) |
|
|
except Exception as e: |
|
|
return f'❌ Error reading supplier data: {str(e)}' |
|
|
|
|
|
state.text_data = text_data |
|
|
return "✅ Data processed successfully" |
|
|
except Exception as e: |
|
|
return f'❌ Error processing data: {str(e)}' |
|
|
|
|
|
def perform_demand_forecasting(state): |
|
|
if state.sales_df is None: |
|
|
return "Error: No sales data provided", None, "Please upload sales data first" |
|
|
|
|
|
try: |
|
|
sales_summary = state.sales_df.describe().to_string() |
|
|
prompt = f"""Analyze the following sales data summary and provide: |
|
|
1. A detailed demand forecast for the next quarter |
|
|
2. Key trends and seasonality patterns |
|
|
3. Actionable recommendations |
|
|
|
|
|
Data Summary: |
|
|
{sales_summary} |
|
|
|
|
|
Please structure your response with clear sections for Forecast, Trends, and Recommendations.""" |
|
|
|
|
|
response = model.generate_content(prompt) |
|
|
analysis_text = response.text |
|
|
|
|
|
fig = px.line(state.sales_df, title='Historical Sales Data and Forecast') |
|
|
fig.update_layout( |
|
|
template='plotly_dark', |
|
|
title_x=0.5, |
|
|
title_font_size=20, |
|
|
showlegend=True, |
|
|
hovermode='x', |
|
|
paper_bgcolor='#2d2d2d', |
|
|
plot_bgcolor='#363636', |
|
|
font=dict(color='white') |
|
|
) |
|
|
|
|
|
return analysis_text, fig, "✅ Analysis completed successfully" |
|
|
except Exception as e: |
|
|
return f"❌ Error in demand forecasting: {str(e)}", None, "Analysis failed" |
|
|
|
|
|
def perform_risk_assessment(state): |
|
|
if state.supplier_df is None: |
|
|
return "Error: No supplier data provided", None, "Please upload supplier data first" |
|
|
|
|
|
try: |
|
|
supplier_summary = state.supplier_df.describe().to_string() |
|
|
prompt = f"""Perform a comprehensive risk assessment based on: |
|
|
|
|
|
Supplier Data Summary: |
|
|
{supplier_summary} |
|
|
|
|
|
Additional Context: |
|
|
{state.text_data if state.text_data else 'No additional context provided'} |
|
|
|
|
|
Please provide: |
|
|
1. Risk scoring for each supplier |
|
|
2. Identified risk factors |
|
|
3. Mitigation recommendations""" |
|
|
|
|
|
response = model.generate_content(prompt) |
|
|
analysis_text = response.text |
|
|
|
|
|
fig = px.scatter(state.supplier_df, title='Supplier Risk Assessment') |
|
|
fig.update_layout( |
|
|
template='plotly_dark', |
|
|
title_x=0.5, |
|
|
title_font_size=20, |
|
|
showlegend=True, |
|
|
hovermode='closest', |
|
|
paper_bgcolor='#2d2d2d', |
|
|
plot_bgcolor='#363636', |
|
|
font=dict(color='white') |
|
|
) |
|
|
|
|
|
return analysis_text, fig, "✅ Risk assessment completed" |
|
|
except Exception as e: |
|
|
return f"❌ Error in risk assessment: {str(e)}", None, "Assessment failed" |
|
|
|
|
|
def perform_inventory_optimization(state): |
|
|
if state.sales_df is None: |
|
|
return "Error: No sales data provided", None, "Please upload sales data first" |
|
|
|
|
|
try: |
|
|
inventory_summary = state.sales_df.describe().to_string() |
|
|
prompt = f"""Analyze the following inventory data and provide: |
|
|
1. Optimal inventory levels |
|
|
2. Reorder points |
|
|
3. Safety stock recommendations |
|
|
4. ABC analysis insights |
|
|
|
|
|
Data Summary: |
|
|
{inventory_summary} |
|
|
|
|
|
Additional Context: |
|
|
{state.text_data if state.text_data else 'No additional context provided'} |
|
|
|
|
|
Please structure your response with clear sections for each aspect.""" |
|
|
|
|
|
response = model.generate_content(prompt) |
|
|
analysis_text = response.text |
|
|
|
|
|
fig = go.Figure() |
|
|
|
|
|
if 'quantity' in state.sales_df.columns: |
|
|
fig.add_trace(go.Scatter( |
|
|
y=state.sales_df['quantity'], |
|
|
name='Inventory Level', |
|
|
line=dict(color='#3498DB') |
|
|
)) |
|
|
|
|
|
fig.update_layout( |
|
|
title='Inventory Level Analysis', |
|
|
template='plotly_dark', |
|
|
title_x=0.5, |
|
|
title_font_size=20, |
|
|
showlegend=True, |
|
|
hovermode='x', |
|
|
paper_bgcolor='#2d2d2d', |
|
|
plot_bgcolor='#363636', |
|
|
font=dict(color='white') |
|
|
) |
|
|
|
|
|
return analysis_text, fig, "✅ Inventory optimization completed" |
|
|
except Exception as e: |
|
|
return f"❌ Error in inventory optimization: {str(e)}", None, "Analysis failed" |
|
|
|
|
|
def perform_supplier_performance(state): |
|
|
if state.supplier_df is None: |
|
|
return "Error: No supplier data provided", None, "Please upload supplier data first" |
|
|
|
|
|
try: |
|
|
supplier_summary = state.supplier_df.describe().to_string() |
|
|
prompt = f"""Analyze supplier performance based on: |
|
|
|
|
|
Supplier Data Summary: |
|
|
{supplier_summary} |
|
|
|
|
|
Additional Context: |
|
|
{state.text_data if state.text_data else 'No additional context provided'} |
|
|
|
|
|
Please provide: |
|
|
1. Supplier performance metrics |
|
|
2. Performance rankings |
|
|
3. Areas for improvement |
|
|
4. Supplier development recommendations""" |
|
|
|
|
|
response = model.generate_content(prompt) |
|
|
analysis_text = response.text |
|
|
|
|
|
if 'performance_score' in state.supplier_df.columns: |
|
|
fig = px.box(state.supplier_df, y='performance_score', |
|
|
title='Supplier Performance Distribution') |
|
|
else: |
|
|
fig = go.Figure(data=[ |
|
|
go.Bar(name='On-Time Delivery', x=['Supplier A', 'Supplier B', 'Supplier C'], |
|
|
y=[95, 87, 92]), |
|
|
go.Bar(name='Quality Score', x=['Supplier A', 'Supplier B', 'Supplier C'], |
|
|
y=[88, 94, 90]) |
|
|
]) |
|
|
|
|
|
fig.update_layout( |
|
|
template='plotly_dark', |
|
|
title_x=0.5, |
|
|
title_font_size=20, |
|
|
showlegend=True, |
|
|
paper_bgcolor='#2d2d2d', |
|
|
plot_bgcolor='#363636', |
|
|
font=dict(color='white') |
|
|
) |
|
|
|
|
|
return analysis_text, fig, "✅ Supplier performance analysis completed" |
|
|
except Exception as e: |
|
|
return f"❌ Error in supplier performance analysis: {str(e)}", None, "Analysis failed" |
|
|
|
|
|
def perform_sustainability_analysis(state): |
|
|
if state.supplier_df is None and state.sales_df is None: |
|
|
return "Error: No data provided", None, "Please upload data first" |
|
|
|
|
|
try: |
|
|
data_summary = "" |
|
|
if state.supplier_df is not None: |
|
|
data_summary += f"Supplier Data Summary:\n{state.supplier_df.describe().to_string()}\n\n" |
|
|
if state.sales_df is not None: |
|
|
data_summary += f"Sales Data Summary:\n{state.sales_df.describe().to_string()}" |
|
|
|
|
|
prompt = f"""Perform a comprehensive sustainability analysis: |
|
|
|
|
|
Data Summary: |
|
|
{data_summary} |
|
|
|
|
|
Additional Context: |
|
|
{state.text_data if state.text_data else 'No additional context provided'} |
|
|
|
|
|
Please provide: |
|
|
1. Carbon footprint analysis |
|
|
2. Environmental impact metrics |
|
|
3. Sustainability recommendations |
|
|
4. Green initiative opportunities |
|
|
5. ESG performance indicators""" |
|
|
|
|
|
response = model.generate_content(prompt) |
|
|
analysis_text = response.text |
|
|
|
|
|
fig = go.Figure() |
|
|
|
|
|
categories = ['Carbon Emissions', 'Water Usage', 'Waste Reduction', |
|
|
'Energy Efficiency', 'Green Transportation'] |
|
|
current_scores = [75, 82, 68, 90, 60] |
|
|
target_scores = [100, 100, 100, 100, 100] |
|
|
|
|
|
fig.add_trace(go.Scatterpolar( |
|
|
r=current_scores, |
|
|
theta=categories, |
|
|
fill='toself', |
|
|
name='Current Performance' |
|
|
)) |
|
|
fig.add_trace(go.Scatterpolar( |
|
|
r=target_scores, |
|
|
theta=categories, |
|
|
fill='toself', |
|
|
name='Target' |
|
|
)) |
|
|
|
|
|
fig.update_layout( |
|
|
polar=dict( |
|
|
radialaxis=dict( |
|
|
visible=True, |
|
|
range=[0, 100] |
|
|
)), |
|
|
showlegend=True, |
|
|
title='Sustainability Performance Metrics', |
|
|
template='plotly_dark', |
|
|
title_x=0.5, |
|
|
title_font_size=20, |
|
|
paper_bgcolor='#2d2d2d', |
|
|
plot_bgcolor='#363636', |
|
|
font=dict(color='white') |
|
|
) |
|
|
|
|
|
return analysis_text, fig, "✅ Sustainability analysis completed" |
|
|
except Exception as e: |
|
|
return f"❌ Error in sustainability analysis: {str(e)}", None, "Analysis failed" |
|
|
|
|
|
def calculate_shipping_cost(base_cost, params): |
|
|
"""Calculate total shipping cost with all factors""" |
|
|
total_cost = base_cost |
|
|
|
|
|
|
|
|
fuel_charge = base_cost * (params['fuel_surcharge'] / 100) |
|
|
|
|
|
|
|
|
insurance = params['line_item_value'] * (params['insurance_rate'] / 100) |
|
|
|
|
|
|
|
|
duty = params['line_item_value'] * (params['customs_duty'] / 100) |
|
|
|
|
|
|
|
|
handling_charges = 0 |
|
|
handling_rates = { |
|
|
"Temperature Controlled": 0.15, |
|
|
"Hazardous Materials": 0.25, |
|
|
"Fragile Items": 0.10, |
|
|
"Express Delivery": 0.20, |
|
|
"Door-to-Door Service": 0.15 |
|
|
} |
|
|
|
|
|
for requirement in params['special_handling']: |
|
|
if requirement in handling_rates: |
|
|
handling_charges += base_cost * handling_rates[requirement] |
|
|
|
|
|
|
|
|
distance_rate = { |
|
|
"Air": 0.1, |
|
|
"Ocean": 0.05, |
|
|
"Truck": 0.15 |
|
|
} |
|
|
distance_charge = params['distance'] * distance_rate[params['shipment_mode']] |
|
|
|
|
|
|
|
|
transit_charge = params['transit_time'] * (base_cost * 0.01) |
|
|
|
|
|
total_cost = base_cost + fuel_charge + insurance + duty + handling_charges + distance_charge + transit_charge |
|
|
|
|
|
return { |
|
|
'base_cost': round(base_cost, 2), |
|
|
'fuel_charge': round(fuel_charge, 2), |
|
|
'insurance': round(insurance, 2), |
|
|
'customs_duty': round(duty, 2), |
|
|
'handling_charges': round(handling_charges, 2), |
|
|
'distance_charge': round(distance_charge, 2), |
|
|
'transit_charge': round(transit_charge, 2), |
|
|
'total_cost': round(total_cost, 2) |
|
|
} |
|
|
|
|
|
def predict_freight_cost(state, params): |
|
|
"""Predict freight cost with enhanced parameters""" |
|
|
if state.freight_model is None: |
|
|
return "Error: Freight prediction model not loaded" |
|
|
|
|
|
try: |
|
|
|
|
|
mode = params['shipment_mode'].replace("✈️ ", "").replace("🚢 ", "").replace("🚛 ", "") |
|
|
|
|
|
|
|
|
features = { |
|
|
'weight (kilograms)': params['weight'], |
|
|
'line item value': params['line_item_value'], |
|
|
'cost per kilogram': params['cost_per_kg'], |
|
|
'shipment mode_Air Charter_weight': params['weight'] if mode == "Air" else 0, |
|
|
'shipment mode_Ocean_weight': params['weight'] if mode == "Ocean" else 0, |
|
|
'shipment mode_Truck_weight': params['weight'] if mode == "Truck" else 0, |
|
|
'shipment mode_Air Charter_line_item_value': params['line_item_value'] if mode == "Air" else 0, |
|
|
'shipment mode_Ocean_line_item_value': params['line_item_value'] if mode == "Ocean" else 0, |
|
|
'shipment mode_Truck_line_item_value': params['line_item_value'] if mode == "Truck" else 0 |
|
|
} |
|
|
|
|
|
input_data = pd.DataFrame([features]) |
|
|
base_prediction = state.freight_model.predict(input_data)[0] |
|
|
|
|
|
|
|
|
cost_breakdown = calculate_shipping_cost(base_prediction, params) |
|
|
|
|
|
return cost_breakdown |
|
|
|
|
|
except Exception as e: |
|
|
return f"Error making prediction: {str(e)}" |
|
|
if state.freight_model is None: |
|
|
return "Error: Freight prediction model not loaded" |
|
|
|
|
|
try: |
|
|
|
|
|
if "Air" in shipment_mode: |
|
|
air_charter_weight = weight |
|
|
air_charter_value = line_item_value |
|
|
elif "Ocean" in shipment_mode: |
|
|
ocean_weight = weight |
|
|
ocean_value = line_item_value |
|
|
else: |
|
|
truck_weight = weight |
|
|
truck_value = line_item_value |
|
|
|
|
|
features = { |
|
|
'weight (kilograms)': weight, |
|
|
'line item value': line_item_value, |
|
|
'cost per kilogram': cost_per_kg, |
|
|
'shipment mode_Air Charter_weight': air_charter_weight, |
|
|
'shipment mode_Ocean_weight': ocean_weight, |
|
|
'shipment mode_Truck_weight': truck_weight, |
|
|
'shipment mode_Air Charter_line_item_value': air_charter_value, |
|
|
'shipment mode_Ocean_line_item_value': ocean_value, |
|
|
'shipment mode_Truck_line_item_value': truck_value |
|
|
} |
|
|
input_data = pd.DataFrame([features]) |
|
|
|
|
|
prediction = state.freight_model.predict(input_data) |
|
|
return round(float(prediction[0]), 2) |
|
|
except Exception as e: |
|
|
return f"Error making prediction: {str(e)}" |
|
|
if state.freight_model is None: |
|
|
return "Error: Freight prediction model not loaded" |
|
|
|
|
|
try: |
|
|
features = { |
|
|
'weight (kilograms)': weight, |
|
|
'line item value': line_item_value, |
|
|
'cost per kilogram': cost_per_kg, |
|
|
'shipment mode_Air Charter_weight': air_charter_weight if "Air" in shipment_mode else 0, |
|
|
'shipment mode_Ocean_weight': ocean_weight if "Ocean" in shipment_mode else 0, |
|
|
'shipment mode_Truck_weight': truck_weight if "Truck" in shipment_mode else 0, |
|
|
'shipment mode_Air Charter_line_item_value': air_charter_value if "Air" in shipment_mode else 0, |
|
|
'shipment mode_Ocean_line_item_value': ocean_value if "Ocean" in shipment_mode else 0, |
|
|
'shipment mode_Truck_line_item_value': truck_value if "Truck" in shipment_mode else 0 |
|
|
} |
|
|
input_data = pd.DataFrame([features]) |
|
|
|
|
|
prediction = state.freight_model.predict(input_data) |
|
|
return round(float(prediction[0]), 2) |
|
|
except Exception as e: |
|
|
return f"Error making prediction: {str(e)}" |
|
|
|
|
|
def chat_with_navigator(state, message): |
|
|
try: |
|
|
context = "Available data and analysis:\n" |
|
|
if state.sales_df is not None: |
|
|
context += f"- Sales data with {len(state.sales_df)} records\n" |
|
|
if state.supplier_df is not None: |
|
|
context += f"- Supplier data with {len(state.supplier_df)} records\n" |
|
|
if state.text_data: |
|
|
context += "- Additional context from text data\n" |
|
|
if state.freight_predictions: |
|
|
context += f"- Recent freight predictions: {state.freight_predictions[-5:]}\n" |
|
|
|
|
|
if state.analysis_results: |
|
|
context += "\nRecent analysis results:\n" |
|
|
for analysis_type, results in state.analysis_results.items(): |
|
|
context += f"- {analysis_type} completed\n" |
|
|
|
|
|
prompt = f"""You are SupplyChainAI Navigator's assistant. Help the user with supply chain analysis, |
|
|
including demand forecasting, risk assessment, and freight cost predictions. |
|
|
|
|
|
Available Context: |
|
|
{context} |
|
|
|
|
|
Chat History: |
|
|
{str(state.chat_history[-3:]) if state.chat_history else 'No previous messages'} |
|
|
|
|
|
User message: {message} |
|
|
|
|
|
Provide a helpful response based on the available data and analysis results.""" |
|
|
|
|
|
response = chat_model.generate_content(prompt) |
|
|
|
|
|
state.chat_history.append({"role": "user", "content": message}) |
|
|
state.chat_history.append({"role": "assistant", "content": response.text}) |
|
|
|
|
|
return state.chat_history |
|
|
except Exception as e: |
|
|
return [{"role": "assistant", "content": f"Error: {str(e)}"}] |
|
|
|
|
|
def generate_pdf_report(state, analysis_options): |
|
|
try: |
|
|
temp_dir = tempfile.mkdtemp() |
|
|
pdf_path = os.path.join(temp_dir, "supply_chain_report.pdf") |
|
|
|
|
|
doc = SimpleDocTemplate(pdf_path, pagesize=letter) |
|
|
styles = getSampleStyleSheet() |
|
|
story = [] |
|
|
|
|
|
|
|
|
title_style = ParagraphStyle( |
|
|
'CustomTitle', |
|
|
parent=styles['Heading1'], |
|
|
fontSize=24, |
|
|
spaceAfter=30, |
|
|
textColor=colors.HexColor('#2C3E50') |
|
|
) |
|
|
|
|
|
story.append(Paragraph("SupplyChainAI Navigator Report", title_style)) |
|
|
story.append(Spacer(1, 12)) |
|
|
|
|
|
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") |
|
|
story.append(Paragraph(f"Generated on: {timestamp}", styles['Normal'])) |
|
|
story.append(Spacer(1, 20)) |
|
|
|
|
|
if state.analysis_results: |
|
|
for analysis_type, results in state.analysis_results.items(): |
|
|
if analysis_type in analysis_options: |
|
|
story.append(Paragraph(analysis_type, styles['Heading2'])) |
|
|
story.append(Spacer(1, 12)) |
|
|
story.append(Paragraph(results['text'], styles['Normal'])) |
|
|
story.append(Spacer(1, 12)) |
|
|
|
|
|
if 'figure' in results: |
|
|
img_path = os.path.join(temp_dir, f"{analysis_type.lower()}_plot.png") |
|
|
results['figure'].write_image(img_path) |
|
|
story.append(Image(img_path, width=400, height=300)) |
|
|
|
|
|
story.append(Spacer(1, 20)) |
|
|
|
|
|
if state.freight_predictions: |
|
|
story.append(Paragraph("Recent Freight Cost Predictions", styles['Heading2'])) |
|
|
story.append(Spacer(1, 12)) |
|
|
|
|
|
pred_data = [["Prediction #", "Cost (USD)"]] |
|
|
for i, pred in enumerate(state.freight_predictions[-5:], 1): |
|
|
pred_data.append([f"Prediction {i}", f"${pred:,.2f}"]) |
|
|
|
|
|
table = Table(pred_data) |
|
|
table.setStyle(TableStyle([ |
|
|
('BACKGROUND', (0, 0), (-1, 0), colors.HexColor('#3498DB')), |
|
|
('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke), |
|
|
('ALIGN', (0, 0), (-1, -1), 'CENTER'), |
|
|
('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'), |
|
|
('FONTSIZE', (0, 0), (-1, 0), 14), |
|
|
('BOTTOMPADDING', (0, 0), (-1, 0), 12), |
|
|
('BACKGROUND', (0, 1), (-1, -1), colors.whitesmoke), |
|
|
('TEXTCOLOR', (0, 1), (-1, -1), colors.black), |
|
|
('FONTNAME', (0, 1), (-1, -1), 'Helvetica'), |
|
|
('FONTSIZE', (0, 1), (-1, -1), 12), |
|
|
('GRID', (0, 0), (-1, -1), 1, colors.black) |
|
|
])) |
|
|
story.append(table) |
|
|
story.append(Spacer(1, 20)) |
|
|
|
|
|
doc.build(story) |
|
|
return pdf_path |
|
|
except Exception as e: |
|
|
print(f"Error generating PDF: {str(e)}") |
|
|
return None |
|
|
|
|
|
def run_analyses(state, choices, sales_file, supplier_file, text_data): |
|
|
results = [] |
|
|
figures = [] |
|
|
status_messages = [] |
|
|
|
|
|
process_status = process_uploaded_data(state, sales_file, supplier_file, text_data) |
|
|
if "Error" in process_status: |
|
|
return process_status, None, process_status |
|
|
|
|
|
for choice in choices: |
|
|
if "📈 Demand Forecasting" in choice: |
|
|
text, fig, status = perform_demand_forecasting(state) |
|
|
results.append(text) |
|
|
figures.append(fig) |
|
|
status_messages.append(status) |
|
|
if text and fig: |
|
|
state.analysis_results['Demand Forecasting'] = {'text': text, 'figure': fig} |
|
|
|
|
|
elif "⚠️ Risk Assessment" in choice: |
|
|
text, fig, status = perform_risk_assessment(state) |
|
|
results.append(text) |
|
|
figures.append(fig) |
|
|
status_messages.append(status) |
|
|
if text and fig: |
|
|
state.analysis_results['Risk Assessment'] = {'text': text, 'figure': fig} |
|
|
|
|
|
elif "📦 Inventory Optimization" in choice: |
|
|
text, fig, status = perform_inventory_optimization(state) |
|
|
results.append(text) |
|
|
figures.append(fig) |
|
|
status_messages.append(status) |
|
|
if text and fig: |
|
|
state.analysis_results['Inventory Optimization'] = {'text': text, 'figure': fig} |
|
|
|
|
|
elif "🤝 Supplier Performance" in choice: |
|
|
text, fig, status = perform_supplier_performance(state) |
|
|
results.append(text) |
|
|
figures.append(fig) |
|
|
status_messages.append(status) |
|
|
if text and fig: |
|
|
state.analysis_results['Supplier Performance'] = {'text': text, 'figure': fig} |
|
|
|
|
|
elif "🌿 Sustainability Analysis" in choice: |
|
|
text, fig, status = perform_sustainability_analysis(state) |
|
|
results.append(text) |
|
|
figures.append(fig) |
|
|
status_messages.append(status) |
|
|
if text and fig: |
|
|
state.analysis_results['Sustainability Analysis'] = {'text': text, 'figure': fig} |
|
|
|
|
|
combined_results = "\n\n".join(results) |
|
|
combined_status = "\n".join(status_messages) |
|
|
|
|
|
final_figure = figures[-1] if figures else None |
|
|
|
|
|
return combined_results, final_figure, combined_status |
|
|
|
|
|
def predict_and_store_freight(state, *args): |
|
|
if len(args) >= 3: |
|
|
weight, line_item_value, shipment_mode = args[:3] |
|
|
result = predict_freight_cost(state, weight, line_item_value, 50, shipment_mode) |
|
|
if isinstance(result, (int, float)): |
|
|
state.freight_predictions.append(result) |
|
|
return result |
|
|
return "Error: Invalid parameters" |
|
|
|
|
|
def create_interface(): |
|
|
state = SupplyChainState() |
|
|
|
|
|
with gr.Blocks(css=CUSTOM_CSS, title="SupplyChainAI Navigator") as demo: |
|
|
|
|
|
with gr.Row(elem_classes="main-header"): |
|
|
with gr.Column(): |
|
|
gr.Markdown("# 🚢 SupplyChainAI Navigator", elem_classes="app-title") |
|
|
gr.Markdown("### Intelligent Supply Chain Analysis & Optimization", elem_classes="app-subtitle") |
|
|
gr.Markdown("An AI-powered platform for comprehensive supply chain analytics", elem_classes="app-description") |
|
|
gr.Markdown("### Created by Aditya Ratan", elem_classes="creator-info") |
|
|
|
|
|
|
|
|
with gr.Tabs() as tabs: |
|
|
|
|
|
with gr.Tab("📊 Data Upload", elem_classes="tab-content"): |
|
|
with gr.Row(): |
|
|
with gr.Column(scale=1): |
|
|
sales_data_upload = gr.File( |
|
|
file_types=[".xlsx", ".xls", ".csv"], |
|
|
label="📈 Sales Data (Excel or CSV)", |
|
|
elem_classes="file-upload" |
|
|
) |
|
|
gr.Markdown("*Upload sales data in Excel (.xlsx, .xls) or CSV format*", elem_classes="file-instructions") |
|
|
|
|
|
with gr.Column(scale=1): |
|
|
supplier_data_upload = gr.File( |
|
|
file_types=[".xlsx", ".xls", ".csv"], |
|
|
label="🏭 Supplier Data (Excel or CSV)", |
|
|
elem_classes="file-upload" |
|
|
) |
|
|
gr.Markdown("*Upload supplier data in Excel (.xlsx, .xls) or CSV format*", elem_classes="file-instructions") |
|
|
|
|
|
with gr.Row(): |
|
|
text_input_area = gr.Textbox( |
|
|
label="📝 Additional Context", |
|
|
placeholder="Add market updates, news, or other relevant information...", |
|
|
lines=5 |
|
|
) |
|
|
|
|
|
with gr.Row(): |
|
|
upload_status = gr.Textbox( |
|
|
label="Status", |
|
|
elem_classes="status-box" |
|
|
) |
|
|
upload_button = gr.Button( |
|
|
"🔄 Process Data", |
|
|
variant="primary", |
|
|
elem_classes="action-button" |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Tab("🔍 Analysis", elem_classes="tab-content"): |
|
|
with gr.Row(): |
|
|
analysis_options = gr.CheckboxGroup( |
|
|
choices=[ |
|
|
"📈 Demand Forecasting", |
|
|
"⚠️ Risk Assessment", |
|
|
"📦 Inventory Optimization", |
|
|
"🤝 Supplier Performance", |
|
|
"🌿 Sustainability Analysis" |
|
|
], |
|
|
label="Choose analyses to perform", |
|
|
value=[] |
|
|
) |
|
|
|
|
|
analyze_button = gr.Button( |
|
|
"🚀 Run Analysis", |
|
|
variant="primary", |
|
|
elem_classes="action-button" |
|
|
) |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(scale=2): |
|
|
analysis_output = gr.Textbox( |
|
|
label="Analysis Results", |
|
|
elem_classes="result-box" |
|
|
) |
|
|
with gr.Column(scale=3): |
|
|
plot_output = gr.Plot( |
|
|
label="Visualization", |
|
|
elem_classes="chart-container" |
|
|
) |
|
|
processing_status = gr.Textbox( |
|
|
label="Processing Status", |
|
|
elem_classes="status-box" |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Tab("💰 Cost Prediction", elem_classes="tab-content"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
shipment_mode = gr.Dropdown( |
|
|
choices=["✈️ Air", "🚢 Ocean", "🚛 Truck"], |
|
|
label="Transport Mode", |
|
|
value="✈️ Air" |
|
|
) |
|
|
|
|
|
|
|
|
weight = gr.Slider( |
|
|
label="📦 Weight (kg)", |
|
|
minimum=1, |
|
|
maximum=10000, |
|
|
step=1, |
|
|
value=1000 |
|
|
) |
|
|
line_item_value = gr.Slider( |
|
|
label="💵 Item Value (USD)", |
|
|
minimum=1, |
|
|
maximum=1000000, |
|
|
step=1, |
|
|
value=10000 |
|
|
) |
|
|
cost_per_kg = gr.Slider( |
|
|
label="💲 Base Cost per kg (USD)", |
|
|
minimum=1, |
|
|
maximum=500, |
|
|
step=1, |
|
|
value=50 |
|
|
) |
|
|
|
|
|
|
|
|
gr.Markdown("### Advanced Parameters") |
|
|
transit_time = gr.Slider( |
|
|
label="🕒 Transit Time (Days)", |
|
|
minimum=1, |
|
|
maximum=60, |
|
|
step=1, |
|
|
value=7 |
|
|
) |
|
|
distance = gr.Slider( |
|
|
label="📏 Distance (km)", |
|
|
minimum=100, |
|
|
maximum=20000, |
|
|
step=100, |
|
|
value=1000 |
|
|
) |
|
|
fuel_surcharge = gr.Slider( |
|
|
label="⛽ Fuel Surcharge (%)", |
|
|
minimum=0, |
|
|
maximum=50, |
|
|
step=0.5, |
|
|
value=5 |
|
|
) |
|
|
|
|
|
|
|
|
gr.Markdown("### Risk Factors") |
|
|
insurance_rate = gr.Slider( |
|
|
label="🛡️ Insurance Rate (%)", |
|
|
minimum=0.1, |
|
|
maximum=10, |
|
|
step=0.1, |
|
|
value=1 |
|
|
) |
|
|
customs_duty = gr.Slider( |
|
|
label="🏛️ Customs Duty (%)", |
|
|
minimum=0, |
|
|
maximum=40, |
|
|
step=0.5, |
|
|
value=5 |
|
|
) |
|
|
|
|
|
|
|
|
gr.Markdown("### Special Handling") |
|
|
special_handling = gr.CheckboxGroup( |
|
|
choices=[ |
|
|
"Temperature Controlled", |
|
|
"Hazardous Materials", |
|
|
"Fragile Items", |
|
|
"Express Delivery", |
|
|
"Door-to-Door Service" |
|
|
], |
|
|
label="Special Requirements" |
|
|
) |
|
|
|
|
|
predict_button = gr.Button( |
|
|
"🔍 Calculate Total Cost", |
|
|
variant="primary", |
|
|
elem_classes="action-button" |
|
|
) |
|
|
with gr.Row(): |
|
|
freight_result = gr.Number( |
|
|
label="Base Freight Cost (USD)", |
|
|
elem_classes="result-box" |
|
|
) |
|
|
total_cost = gr.Number( |
|
|
label="Total Cost Including All Charges (USD)", |
|
|
elem_classes="result-box" |
|
|
) |
|
|
cost_breakdown = gr.JSON( |
|
|
label="Cost Breakdown", |
|
|
elem_classes="result-box" |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Tab("💬 Chat", elem_classes="tab-content"): |
|
|
chatbot = gr.Chatbot( |
|
|
label="Chat History", |
|
|
elem_classes="chat-container", |
|
|
height=400 |
|
|
) |
|
|
with gr.Row(): |
|
|
msg = gr.Textbox( |
|
|
label="Message", |
|
|
placeholder="Ask about your supply chain data...", |
|
|
scale=4 |
|
|
) |
|
|
chat_button = gr.Button( |
|
|
"📤 Send", |
|
|
variant="primary", |
|
|
scale=1, |
|
|
elem_classes="action-button" |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Tab("📑 Report", elem_classes="tab-content"): |
|
|
report_options = gr.CheckboxGroup( |
|
|
choices=[ |
|
|
"📈 Demand Forecasting", |
|
|
"⚠️ Risk Assessment", |
|
|
"📦 Inventory Optimization", |
|
|
"🤝 Supplier Performance", |
|
|
"🌿 Sustainability Analysis" |
|
|
], |
|
|
label="Select sections to include", |
|
|
value=[] |
|
|
) |
|
|
report_button = gr.Button( |
|
|
"📄 Generate Report", |
|
|
variant="primary", |
|
|
elem_classes="action-button" |
|
|
) |
|
|
report_download = gr.File( |
|
|
label="Download Report" |
|
|
) |
|
|
|
|
|
|
|
|
upload_button.click( |
|
|
fn=lambda *args: process_uploaded_data(state, *args), |
|
|
inputs=[sales_data_upload, supplier_data_upload, text_input_area], |
|
|
outputs=[upload_status]) |
|
|
|
|
|
analyze_button.click( |
|
|
fn=lambda choices, sales, supplier, text: run_analyses(state, choices, sales, supplier, text), |
|
|
inputs=[analysis_options, sales_data_upload, supplier_data_upload, text_input_area], |
|
|
outputs=[analysis_output, plot_output, processing_status] |
|
|
) |
|
|
|
|
|
predict_button.click( |
|
|
fn=lambda mode, w, val, cost, time, dist, fuel, ins, duty, special: predict_and_store_freight( |
|
|
state, |
|
|
{ |
|
|
'shipment_mode': mode, |
|
|
'weight': w, |
|
|
'line_item_value': val, |
|
|
'cost_per_kg': cost, |
|
|
'transit_time': time, |
|
|
'distance': dist, |
|
|
'fuel_surcharge': fuel, |
|
|
'insurance_rate': ins, |
|
|
'customs_duty': duty, |
|
|
'special_handling': special |
|
|
} |
|
|
), |
|
|
inputs=[ |
|
|
shipment_mode, weight, line_item_value, cost_per_kg, |
|
|
transit_time, distance, fuel_surcharge, |
|
|
insurance_rate, customs_duty, special_handling |
|
|
], |
|
|
outputs=[freight_result, total_cost, cost_breakdown] |
|
|
) |
|
|
|
|
|
chat_button.click( |
|
|
fn=lambda message: chat_with_navigator(state, message), |
|
|
inputs=[msg], |
|
|
outputs=[chatbot] |
|
|
) |
|
|
|
|
|
report_button.click( |
|
|
fn=lambda options: generate_pdf_report(state, options), |
|
|
inputs=[report_options], |
|
|
outputs=[report_download] |
|
|
) |
|
|
|
|
|
|
|
|
gr.HTML( |
|
|
'''<div class="footer"> |
|
|
Made with 🧠 by Aditya Ratan |
|
|
</div>''' |
|
|
) |
|
|
|
|
|
return demo |
|
|
|
|
|
if __name__ == "__main__": |
|
|
demo = create_interface() |
|
|
demo.launch( |
|
|
server_name="0.0.0.0", |
|
|
server_port=7860, |
|
|
debug=True |
|
|
) |
