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import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import seaborn as sns
import gradio as gr
import sqlite3
from datetime import datetime, timedelta
def generate_sample_data():
# Generate sample data
np.random.seed(42)
n_customers = 1000
days_ago = [int(x) for x in np.random.randint(0, 365, n_customers)]
crm_data = pd.DataFrame({
'customer_id': range(1, n_customers + 1),
'interactions': np.random.randint(1, 100, n_customers),
'transactions': np.random.uniform(10, 1000, n_customers),
'converted': np.random.choice([0, 1], n_customers, p=[0.7, 0.3]),
'timestamp': [datetime.now() - timedelta(days=d) for d in days_ago]
})
social_days = [int(x) for x in np.random.randint(0, 365, n_customers)]
social_data = pd.DataFrame({
'customer_id': range(1, n_customers + 1),
'interactions': np.random.randint(1, 200, n_customers),
'open_rate': np.random.uniform(0.1, 0.9, n_customers),
'timestamp': [datetime.now() - timedelta(days=d) for d in social_days]
})
# Enhanced financial data with more relevant metrics
financial_days = [int(x) for x in np.random.randint(0, 365, n_customers)]
financial_data = pd.DataFrame({
'customer_id': range(1, n_customers + 1),
'transaction_amount': np.random.uniform(50, 5000, n_customers),
'transaction_frequency': np.random.randint(1, 20, n_customers), # New column
'average_purchase': np.random.uniform(100, 2000, n_customers), # New column
'total_spend': np.random.uniform(1000, 50000, n_customers), # New column
'transaction_date': [datetime.now() - timedelta(days=d) for d in financial_days]
})
return crm_data, social_data, financial_data
def init_database():
conn = sqlite3.connect('sales_intelligence.db')
cursor = conn.cursor()
# Create tables if they don't exist
cursor.execute('''
CREATE TABLE IF NOT EXISTS financial_data (
id INTEGER PRIMARY KEY AUTOINCREMENT,
customer_id INTEGER,
transaction_amount FLOAT,
transaction_frequency INTEGER,
average_purchase FLOAT,
total_spend FLOAT,
transaction_date DATETIME
)
''')
cursor.execute('''
CREATE TABLE IF NOT EXISTS crm_data (
id INTEGER PRIMARY KEY AUTOINCREMENT,
customer_id INTEGER,
interactions INTEGER,
transactions FLOAT,
converted INTEGER,
timestamp DATETIME
)
''')
cursor.execute('''
CREATE TABLE IF NOT EXISTS social_media_data (
id INTEGER PRIMARY KEY AUTOINCREMENT,
customer_id INTEGER,
interactions INTEGER,
open_rate FLOAT,
timestamp DATETIME
)
''')
# Generate and insert sample data
crm_data, social_data, financial_data = generate_sample_data()
try:
crm_data.to_sql('crm_data', conn, if_exists='replace', index=False)
social_data.to_sql('social_media_data', conn, if_exists='replace', index=False)
financial_data.to_sql('financial_data', conn, if_exists='replace', index=False)
print(f"Inserted {len(crm_data)} CRM records")
print(f"Inserted {len(social_data)} social media records")
print(f"Inserted {len(financial_data)} financial records")
except sqlite3.Error as e:
print(f"Error inserting data: {e}")
conn.commit()
conn.close()
print("Database initialized with sample data!")
def segment_prospects(df, data_source):
print("Segmenting prospects...")
if data_source.lower() == 'financial_databases':
# Special handling for financial data
kmeans = KMeans(n_clusters=3)
df['segment'] = kmeans.fit_predict(df[['transaction_amount', 'transaction_frequency', 'average_purchase']])
segment_labels = ['Low Value', 'Medium Value', 'High Value']
df['segment_label'] = [segment_labels[s] for s in df['segment']]
elif 'interactions' in df.columns and 'transactions' in df.columns:
kmeans = KMeans(n_clusters=3)
df['segment'] = kmeans.fit_predict(df[['interactions', 'transactions']])
print("Columns after segmentation:", df.columns)
return df
def performance_analysis(df, data_source):
print("Analyzing performance...")
insights = {}
if data_source.lower() == 'financial_databases':
# Specific analysis for financial data
if 'segment' in df.columns:
# Overall metrics
insights['overall_metrics'] = {
'total_revenue': float(df['total_spend'].sum()),
'average_transaction': float(df['transaction_amount'].mean()),
'total_customers': len(df),
'average_frequency': float(df['transaction_frequency'].mean())
}
# Segment-specific metrics
segment_metrics = df.groupby('segment').agg({
'transaction_amount': ['mean', 'max'],
'transaction_frequency': 'mean',
'total_spend': 'sum',
'average_purchase': 'mean'
}).round(2)
# Convert the segment metrics to a more readable format
for segment in df['segment'].unique():
insights[f'segment_{segment}'] = {
'avg_transaction': float(segment_metrics.loc[segment, ('transaction_amount', 'mean')]),
'max_transaction': float(segment_metrics.loc[segment, ('transaction_amount', 'max')]),
'avg_frequency': float(segment_metrics.loc[segment, ('transaction_frequency', 'mean')]),
'total_revenue': float(segment_metrics.loc[segment, ('total_spend', 'sum')]),
'avg_purchase': float(segment_metrics.loc[segment, ('average_purchase', 'mean')])
}
return pd.DataFrame.from_dict(insights, orient='index')
else:
# Original analysis for other data sources
if 'segment' in df.columns:
insights = df.groupby('segment').mean()
return insights
return pd.DataFrame()
def load_data(data_source):
conn = sqlite3.connect('sales_intelligence.db')
if data_source.lower() == 'crm':
return pd.read_sql('SELECT * FROM crm_data', conn)
elif data_source.lower() == 'social_media':
return pd.read_sql('SELECT * FROM social_media_data', conn)
elif data_source.lower() == 'financial_databases':
return pd.read_sql('SELECT * FROM financial_data', conn)
else:
return pd.DataFrame()
def preprocess_data(df):
# Add any necessary preprocessing steps here
return df
def predict_lead_conversion(df):
# Example model for lead conversion prediction
X = df[['interactions', 'transactions']]
y = df['converted']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
return model, accuracy
def sales_intelligence_platform(data_source):
print("Processing data source:", data_source)
data = load_data(data_source)
if data.empty:
return {"error": f"No data found for source: {data_source}. Valid sources are: 'CRM', 'social_media', 'financial_databases'"}
data = preprocess_data(data)
data = segment_prospects(data, data_source)
model, accuracy = predict_lead_conversion(data) if data_source.lower() == 'crm' else (None, None)
insights = performance_analysis(data, data_source)
if insights.empty:
return {"error": "Could not generate insights from the data"}
result_dict = insights.to_dict()
# Add some helpful messages
if data_source.lower() == 'financial_databases':
result_dict['analysis_description'] = {
'segment_0': 'Low Value Customers',
'segment_1': 'Medium Value Customers',
'segment_2': 'High Value Customers'
}
return result_dict
# Initialize the database with sample data
init_database()
# Create Gradio interface
iface = gr.Interface(
fn=sales_intelligence_platform,
inputs=gr.Dropdown(
choices=["CRM", "social_media", "financial_databases"],
label="Select Data Source"
),
outputs="json",
title="Sales Intelligence Platform",
description="A platform powered by AI to manage sales data and provide insights. Choose a data source to analyze.",
theme="dark"
)
if __name__ == "__main__":
iface.launch() |