script_analysis.py

# script_analysis.py

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import json
import streamlit as st
from utils import client
import plotly.graph_objs as go
import plotly.express as px
from plotly.subplots import make_subplots


def analyze_script(thread_id, additional_context=None):
    run = client.beta.threads.runs.create(
        thread_id=thread_id,
        assistant_id="asst_0TVOqfDUPuaSxtea11xa7DB0"
    )
    
    while run.status in ['queued', 'in_progress', 'cancelling']:
        run = client.beta.threads.runs.retrieve(
            thread_id=thread_id,
            run_id=run.id
        )
    
    if run.status == 'completed':
        messages = client.beta.threads.messages.list(thread_id=thread_id)
        analysis = next((msg.content[0].text.value for msg in reversed(list(messages)) if msg.role == "assistant"), "")
        return analysis
    else:
        return f"Error: Run status is {run.status}"



def process_script_analysis(analysis):
    try:
        # Print raw data for debugging
        st.write("Raw data:")
        st.write(analysis)

        # Parse JSON data
        data = json.loads(analysis)

        # Create a list to hold all script elements
        script_elements = []

        # Define the stages and attributes we're interested in
        stages = ["Introduction", "Rising Action", "Midpoint", "Complications", "Climax", "Falling Action", "Resolution"]
        attributes = ["intensity", "narrative_intensity", "pacing", "tension", "emotion", "action"]

        # Iterate through all stages
        for stage in stages:
            if stage in data:
                element = data[stage]
            elif stage.replace(" ", "_") in data:  # Check for underscore version
                element = data[stage.replace(" ", "_")]
            else:
                # If stage is missing, create a placeholder with default values
                element = {attr: 0 for attr in attributes}
            
            element['stage'] = stage
            script_elements.append(element)

        # Create DataFrame
        df = pd.DataFrame(script_elements)

        # Check if we have data for all stages and attributes
        missing_stages = set(stages) - set(df['stage'])
        missing_attributes = set(attributes) - set(df.columns)

        if missing_stages:
            st.warning(f"Missing data for stages: {', '.join(missing_stages)}")
        if missing_attributes:
            st.warning(f"Missing data for attributes: {', '.join(missing_attributes)}")

        # Ensure all required columns are present
        for attr in attributes:
            if attr not in df.columns:
                df[attr] = 0  # or some default value

        # Display the DataFrame
        st.write("### Processed data:")
        st.dataframe(df)

        # Create an interactive line chart for all attributes
        st.write("### Script Attributes Across Stages")
        fig = go.Figure()
        for attr in attributes:
            fig.add_trace(go.Scatter(x=df['stage'], y=df[attr], mode='lines+markers', name=attr.capitalize()))
        fig.update_layout(title='Script Attributes Across Stages', xaxis_title='Stage', yaxis_title='Score')
        st.plotly_chart(fig, use_container_width=True)

        # Create an interactive heatmap
        st.write("### Heatmap of Script Attributes")
        heatmap_data = df.set_index('stage')[attributes]
        fig = px.imshow(heatmap_data, 
                        labels=dict(x="Attributes", y="Stages", color="Score"),
                        x=attributes,
                        y=heatmap_data.index,
                        color_continuous_scale="YlOrRd")
        fig.update_layout(title='Script Attributes Heatmap')
        st.plotly_chart(fig, use_container_width=True)

        # Create interactive radar charts for each stage
        st.write("### Radar Charts for Each Stage")
        for _, row in df.iterrows():
            stage = row['stage']
            values = row[attributes].values
            
            fig = go.Figure(data=go.Scatterpolar(
                r=values,
                theta=attributes,
                fill='toself'
            ))
            fig.update_layout(
                polar=dict(radialaxis=dict(visible=True, range=[0, 1])),
                showlegend=False,
                title=f"Attributes for {stage}"
            )
            st.plotly_chart(fig, use_container_width=True)

        # Create a stacked bar chart to compare stages
        st.write("### Stage Comparison")
        fig = go.Figure()
        for attr in attributes:
            fig.add_trace(go.Bar(x=df['stage'], y=df[attr], name=attr.capitalize()))
        fig.update_layout(barmode='stack', title='Attribute Composition by Stage', 
                          xaxis_title='Stage', yaxis_title='Cumulative Score')
        st.plotly_chart(fig, use_container_width=True)

        # Create a parallel coordinates plot
        st.write("### Parallel Coordinates Plot")
        
        # Create a numeric color scale based on the order of stages
        color_scale = list(range(len(df)))
        
        fig = px.parallel_coordinates(df, color=color_scale,
                                      dimensions=['intensity', 'narrative_intensity', 'pacing', 'tension', 'emotion', 'action'],
                                      color_continuous_scale=px.colors.sequential.Viridis,
                                      color_continuous_midpoint=len(df) // 2)
        
        # Update color axis to show stage names instead of numbers
        fig.update_layout(
            coloraxis_colorbar=dict(
                title="Stage",
                tickvals=color_scale,
                ticktext=df['stage'],
                lenmode="pixels", len=300,
            )
        )
        
        fig.update_layout(title='Parallel Coordinates Plot of Script Attributes')
        st.plotly_chart(fig, use_container_width=True)

        # Additional analysis or insights
        st.write("### Key Insights")
        st.write("Based on the analysis, here are some key insights about the script:")
        
        highest_intensity = df.loc[df['intensity'].idxmax(), 'stage']
        st.write(f"- The highest intensity occurs during the {highest_intensity} stage.")
        
        avg_pacing = df['pacing'].mean()
        st.write(f"- The average pacing of the script is {avg_pacing:.2f} out of 1.")
        
        emotion_variance = df['emotion'].var()
        st.write(f"- The emotional variance throughout the script is {emotion_variance:.2f}, indicating {'a highly varied' if emotion_variance > 0.1 else 'a consistent'} emotional journey.")

    except Exception as e:
        st.error(f"Error processing data for Script Analysis: {e}")
        st.write("Please check the structure of the JSON data:")
        st.json(analysis)