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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
import streamlit as st
def generate_insights(call_data):
"""
Generate ML insights and visualizations from call data
"""
# Convert call data to DataFrame
df = pd.DataFrame(call_data)
# Sentiment distribution pie chart
plt.figure(figsize=(10, 6))
sentiment_counts = df['sentiment'].value_counts()
plt.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%')
plt.title('Sentiment Distribution')
st.pyplot(plt)
plt.close()
# Calculate sentiment trend
df['sentiment_numeric'] = df['sentiment'].map({'POSITIVE': 1, 'NEGATIVE': -1, 'NEUTRAL': 0})
# Simple trend analysis
X = np.array(range(len(df))).reshape(-1, 1)
y = df['sentiment_numeric'].values
model = LinearRegression()
model.fit(X, y)
# Predict trend
trend_score = model.coef_[0]
trend_interpretation = (
"Improving" if trend_score > 0.1 else
"Declining" if trend_score < -0.1 else
"Stable"
)
# Summary metrics
st.subheader("Call Analysis Summary")
st.write(f"Total Calls: {len(df)}")
st.write("Sentiment Breakdown:")
st.write(sentiment_counts)
st.write(f"Sentiment Trend: {trend_interpretation}")
def main():
st.title("Sales Call Insights")
# Placeholder for loading data mechanism
st.write("Insights generation ready.")
if __name__ == "__main__":
main() |