market_analysis.py

import json
import streamlit as st
import matplotlib.pyplot as plt
import plotly.graph_objs as go
import plotly.express as px
import re
from json_repair import repair_json

def extract_week_data(json_str):
    # Find all JSON-like objects in the string
    json_objects = re.findall(r'\{[^{}]*\}', json_str)
    weeks_data = []
    
    for obj in json_objects:
        try:
            # Replace single quotes with double quotes for valid JSON
            obj = obj.replace("'", '"')
            data = json.loads(obj)
            
            # Check if this object contains week data
            if 'week_number' in data and 'domestic_projection' in data and 'international_projection' in data:
                weeks_data.append(data)
        except json.JSONDecodeError:
            continue
    
    return weeks_data

def analyze_and_process_market(analysis):
    st.write("### Market Analysis")

    st.write("Raw analysis:")
    st.code(analysis)

    try:
        box_office_data = repair_json(analysis)
        box_office_data = json.loads(box_office_data)
        weeks = [f"Week {data['week_number']}" for data in box_office_data.values()]
        domestic_projections = [data["domestic_projection"] for data in box_office_data.values()]
        international_projections = [data["international_projection"] / 1000000 for data in box_office_data.values()]  # Convert to millions

        # Create a Streamlit app
        st.title("Box Office Projections Over 8 Weeks")
        st.write("This app visualizes the domestic and international box office projections for each week of a film's release.")

        # Plotting Domestic Projections
        fig_domestic, ax_domestic = plt.subplots(figsize=(10, 6))
        domestic_bars = ax_domestic.bar(weeks, domestic_projections, color='b')
        ax_domestic.set_ylabel('Domestic Earnings (₹ Crores)')
        ax_domestic.set_title('Domestic Box Office Projections')
        for bar in domestic_bars:
            yval = bar.get_height()
            ax_domestic.text(bar.get_x() + bar.get_width() / 2, yval + 0.2, f"{yval:.1f}", ha='center', va='bottom', fontsize=10)
        st.pyplot(fig_domestic)

        # Plotting International Projections
        fig_international, ax_international = plt.subplots(figsize=(10, 6))
        international_bars = ax_international.bar(weeks, international_projections, color='r')
        ax_international.set_ylabel('International Earnings ($ Million)')
        ax_international.set_title('International Box Office Projections')
        for bar in international_bars:
            yval = bar.get_height()
            ax_international.text(bar.get_x() + bar.get_width() / 2, yval + 0.02, f"{yval:.2f}", ha='center', va='bottom', fontsize=10)
        st.pyplot(fig_international)


    except Exception as e:
        st.error(f"Error processing data for Market Analysis: {e}")

    # Display additional analysis text
    additional_text = re.sub(r'\{[^{}]*\}', '', analysis).strip()
    if additional_text:
        st.write("#### Additional Analysis")
        st.write(additional_text)




# import json
# from utils import client
# import streamlit as st
# import plotly.graph_objs as go
# import plotly.express as px
# import re

# def analyze_market(thread_id, additional_context=None):
#     # Note: You might need to create a new assistant for market analysis
#     run = client.beta.threads.runs.create(
#         thread_id=thread_id,
#         assistant_id="asst_ykSNeNu74RsJPkOxLTPYHQ36"  # Replace with the actual assistant ID for market analysis
#     )
    
#     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 usd_to_inr(usd_value):
#     return usd_value * 75

# def extract_number(value):
#     if isinstance(value, (int, float)):
#         return value
#     if isinstance(value, str):
#         return float(re.sub(r'[^\d.]', '', value))
#     return 0

# def process_market_analysis(analysis):
#     st.write("### Market Analysis")
#     st.write(analysis)  # Display the raw analysis first

#     try:
#         # Extract all JSON-like objects from the response
#         json_objects = re.findall(r'\{[^}]+\}', analysis)
        
#         weeks_data = []
#         for json_str in json_objects:
#             try:
#                 # Replace single quotes with double quotes, except within the "factors_influencing" field
#                 json_str = re.sub(r"'([^']*)':", r'"\1":', json_str)
#                 json_str = json_str.replace("'Pellichoopulu'", '"Pellichoopulu"')
                
#                 # Parse the JSON
#                 week_data = json.loads(json_str)
                
#                 # Clean up the data
#                 domestic = extract_number(week_data.get('domestic_projection', 0))
#                 international = extract_number(week_data.get('international_projection', 0))
                
#                 cleaned_data = {
#                     "week_number": week_data.get('week_number', 'Unknown'),
#                     "domestic_projection": domestic,
#                     "international_projection": international,
#                     "factors_influencing": week_data.get('factors_influencing', 'Not specified')
#                 }
#                 weeks_data.append(cleaned_data)
#             except json.JSONDecodeError as e:
#                 st.warning(f"Couldn't parse JSON object: {json_str}\nError: {str(e)}")

#         # Box office projections
#         st.write("#### Box Office Projections")
#         domestic_projections = []
#         international_projections = []
        
#         for week_data in weeks_data:
#             domestic = week_data['domestic_projection']
#             international = week_data['international_projection']
            
#             # Convert to INR
#             domestic_inr = usd_to_inr(domestic)
#             international_inr = usd_to_inr(international)
            
#             domestic_projections.append(domestic_inr)
#             international_projections.append(international_inr)
            
#             st.write(f"**{week_data['week_number']}**")
#             st.write(f"Domestic: ₹{domestic_inr:,.2f}")
#             st.write(f"International: ₹{international_inr:,.2f}")
#             st.write(f"Factors: {week_data['factors_influencing']}")
#             st.write("---")

#         # Visualize box office projections
#         if weeks_data:
#             weeks = [data['week_number'] for data in weeks_data]
#             fig = go.Figure()
#             fig.add_trace(go.Bar(x=weeks, y=domestic_projections, name='Domestic'))
#             fig.add_trace(go.Bar(x=weeks, y=international_projections, name='International'))
#             fig.update_layout(title='Weekly Box Office Projections (in INR)', barmode='group')
#             st.plotly_chart(fig)

#             # Total projections
#             total_domestic = sum(domestic_projections)
#             total_international = sum(international_projections)
            
#             st.write("#### Total Projections")
#             st.write(f"Total Domestic: ₹{total_domestic:,.2f}")
#             st.write(f"Total International: ₹{total_international:,.2f}")
#             st.write(f"Total Global: ₹{total_domestic + total_international:,.2f}")

#             # Pie chart for domestic vs international split
#             fig = px.pie(values=[total_domestic, total_international], 
#                          names=['Domestic', 'International'], 
#                          title='Domestic vs International Box Office Split')
#             st.plotly_chart(fig)
#         else:
#             st.warning("No valid data found for creating visualizations.")

#     except Exception as e:
#         st.error(f"Error processing data for Market Analysis: {e}")
#         st.code(analysis)