Delete ml_insights.py

ml_insights.py

@@ -1,55 +0,0 @@
-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()