Upload dashboard.py

dashboard.py

@@ -0,0 +1,212 @@
+"""import pandas as pd
+import json
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+import numpy as np
+
+def analyze_data():
+    # Load JSON data
+    with open("session_data.json", "r") as file:
+        data = json.load(file)
+
+    # Extract data
+    interactions = data["interactions"]
+    timestamp = data.get("timestamp", "No Timestamp")
+
+    # Parse interactions into a DataFrame for analysis
+    interaction_data = []
+    for interaction in interactions:
+        sentiment = interaction["sentiment"]
+
+        if isinstance(sentiment, list) and len(sentiment) > 0:
+            sentiment_label = sentiment[0]["label"]
+            sentiment_score = sentiment[0]["score"]
+        elif isinstance(sentiment, dict):
+            sentiment_label = sentiment.get("label", "No Sentiment")
+            sentiment_score = sentiment.get("score", 0.0)
+        else:
+            sentiment_label = "No Sentiment"
+            sentiment_score = 0.0
+
+        sentiment_change = interaction["sentiment_change"]
+        recommendations = interaction["product_recommendations"]
+        objection = interaction["objection_handling"]
+
+        interaction_data.append({
+            "Transcription": interaction["transcription"],
+            "Sentiment Label": sentiment_label,
+            "Sentiment Score": sentiment_score,
+            "Sentiment Change": sentiment_change,
+            "Objection": objection[0] if objection else None,
+            "Objection Response": objection[1] if len(objection) > 1 else None,
+            "Recommendations": recommendations,
+        })
+
+    df = pd.DataFrame(interaction_data)
+
+    # Convert list of recommendations to a string (hashable type)
+    df['Recommendations_Str'] = df['Recommendations'].apply(lambda x: ', '.join([rec[0] for rec in x]) if isinstance(x, list) else str(x))
+    df['Interaction Count'] = df.groupby('Recommendations_Str')['Recommendations_Str'].transform('count')
+    df['CLV'] = df['Interaction Count'] * 10  # Example formula
+
+    # Display data
+    print(f"Data Timestamp: {timestamp}")
+    print("\nCustomer Interaction Summary:")
+    print(df)
+
+    # Insights: Sentiment Trends (Pie chart)
+    sentiment_counts = df["Sentiment Label"].value_counts()
+    fig, ax = plt.subplots(figsize=(4, 4))
+    sentiment_counts.plot(kind="pie", autopct="%1.1f%%", ax=ax)
+    ax.set_ylabel("")
+    plt.title("Sentiment Trends")
+    plt.show()
+
+    # Product Recommendations (Bar chart)
+    all_recommendations = [rec[0] for recs in df["Recommendations"] for rec in recs]
+    recommendation_counts = pd.Series(all_recommendations).value_counts()
+    fig, ax = plt.subplots(figsize=(5, 3))
+    recommendation_counts.plot(kind="bar", color="skyblue", ax=ax)
+    ax.set_title("Top Products")
+    plt.show()
+
+    # Predictive Modeling for Sentiment Scores
+    if len(df) > 1:
+        X = np.arange(len(df)).reshape(-1, 1)
+        y = df["Sentiment Score"].fillna(0).values
+
+        model = LinearRegression()
+        model.fit(X, y)
+
+        future_steps = np.arange(len(df), len(df) + 5).reshape(-1, 1)
+        predicted_scores = model.predict(future_steps)
+
+        print("\nPredicted Sentiment Scores (Next 5 Interactions):")
+        print(predicted_scores)
+
+        # Visualization
+        fig, ax = plt.subplots(figsize=(6, 4))
+        ax.plot(range(len(df)), y, label="Actual Scores", marker="o")
+        ax.plot(range(len(df), len(df) + 5), predicted_scores, label="Predicted Scores", linestyle="--", marker="o")
+        ax.set_title("Sentiment Score Trends")
+        ax.set_xlabel("Interaction Index")
+        ax.set_ylabel("Sentiment Score")
+        ax.legend()
+        plt.show()
+
+    # AI Recommendations for Sales Improvement
+   # print("\nAI Recommendations for Sales Improvement:")
+    #print("""
+    #1. Address objections related to pricing and promotions.
+    #2. Highlight the most recommended products to align with customer preferences.
+    #3. Use sentiment trends to identify weak conversation points.
+    #4. Optimize follow-ups based on predicted sentiment scores.
+    #""")
+
+    # Display Customer Lifetime Value (CLV)
+    #print("\nCustomer Lifetime Value (CLV):")
+    #print(df[['Recommendations_Str', 'Interaction Count', 'CLV']])
+
+#if __name__ == "__main__":
+    #analyze_data()"""
+import pandas as pd
+import json
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+import numpy as np
+from wordcloud import WordCloud
+
+def analyze_data(session_data):
+    # Extract data from the passed session data
+    interactions = session_data["interactions"]
+    timestamp = session_data.get("timestamp", "No Timestamp")
+
+    # Parse interactions into a DataFrame
+    interaction_data = []
+    all_transcriptions = []
+    for interaction in interactions:
+        sentiment = interaction["sentiment"]
+
+        if isinstance(sentiment, list) and len(sentiment) > 0:
+            sentiment_label = sentiment[0]["label"]
+            sentiment_score = sentiment[0]["score"]
+        elif isinstance(sentiment, dict):
+            sentiment_label = sentiment.get("label", "No Sentiment")
+            sentiment_score = sentiment.get("score", 0.0)
+        else:
+            sentiment_label = "No Sentiment"
+            sentiment_score = 0.0
+
+        recommendations = interaction["product_recommendations"]
+        objection = interaction["objection_handling"]
+        transcription = interaction["transcription"]
+        all_transcriptions.append(transcription)
+
+        interaction_data.append({
+    "Transcription": transcription,
+    "Sentiment Label": sentiment_label,
+    "Sentiment Score": sentiment_score,
+    "Objection": objection.get("objection") if objection else None,
+    "Objection Response": objection.get("response") if objection else None,
+    "Recommendations": recommendations,
+})
+
+
+    df = pd.DataFrame(interaction_data)
+
+    # Handle insufficient data for sentiment predictions
+    if len(df) <= 1:
+        sentiment_predictions = "Insufficient data for predictions"
+    else:
+        # Predictive Modeling for Sentiment Scores
+        X = np.arange(len(df)).reshape(-1, 1)
+        y = df["Sentiment Score"].fillna(0).values
+
+        model = LinearRegression()
+        model.fit(X, y)
+
+        future_steps = np.arange(len(df), len(df) + 5).reshape(-1, 1)
+        sentiment_predictions = model.predict(future_steps)
+
+    # Generate Insights
+    sentiment_counts = df["Sentiment Label"].value_counts()
+
+    # Create a pie chart for sentiment trends
+    fig_sentiment, ax = plt.subplots(figsize=(4, 4))
+    sentiment_counts.plot(kind="pie", autopct="%1.1f%%", ax=ax)
+    ax.set_ylabel("")
+    plt.title("Sentiment Trends")
+
+    # Create a bar chart for product recommendations
+    all_recommendations = [rec[0] for recs in df["Recommendations"] for rec in recs]
+    recommendation_counts = pd.Series(all_recommendations).value_counts()
+    fig_recommendations, ax = plt.subplots(figsize=(5, 3))
+    recommendation_counts.plot(kind="bar", color="skyblue", ax=ax)
+    ax.set_title("Top Products")
+
+    # Generate a Word Cloud for Transcriptions
+    all_text = " ".join(all_transcriptions)
+    wordcloud = WordCloud(background_color="white").generate(all_text)
+    fig_wordcloud, ax = plt.subplots(figsize=(6, 4))
+    ax.imshow(wordcloud, interpolation="bilinear")
+    ax.axis("off")
+    plt.title("Call Topics")
+
+    # Actionable Recommendations
+    actionable_recommendations = [
+        "Focus on objection handling related to pricing concerns.",
+        "Highlight top-performing products during calls.",
+        "Leverage sentiment trends to adjust tone and messaging.",
+        "Plan follow-ups based on predicted sentiment trends."
+    ]
+
+    # Return results for Streamlit display
+    return {
+        "timestamp": timestamp,
+        "summary_table": df,
+        "sentiment_chart": fig_sentiment,
+        "recommendation_chart": fig_recommendations,
+        "sentiment_predictions": sentiment_predictions,
+        "wordcloud": fig_wordcloud,
+        "actionable_recommendations": actionable_recommendations,
+    }