python code

from flask import Flask, jsonify
import ast

app = Flask(__name__)

@app.route('/convert', methods=['GET'])
def convert_data():
    # Original string data
    original_str = "{'prediction_revenue': 32448.94, 'roi': 62.244699999999995, 'scaled_roi': 0.013106366803463885, 'roi_category': 'Poor', 'llama_output': \"Based on the provided event details, here are some analysis and recommendations from a sponsor's perspective to potentially improve Return On Investment (ROI):\\n\\n### Analysis\\n\\n- **Expected Footfall:** With an expected footfall range of 100 to 500, it indicates that the concert is likely to be a smaller-scale event. This could limit the potential reach and impact compared to larger events.\\n  \\n- **Ticket Price:** At ₹150 per ticket, this sets a relatively high barrier for entry. Higher ticket prices can deter some attendees from participating in the event.\\n\\n- **Event Duration:** Spanning 3 days is quite extensive and might spread out audience engagement too thinly across the duration of the event.\\n\\n- **Sponsor Cost:** The sponsor has committed ₹20,000 to this event, which is a significant investment given the expected scale of the event.\\n\\n- **ROI Category:** Being categorized as 'Poor' indicates that for every rupee spent on marketing and other efforts (including sponsorship), the return in sales or engagement is not meeting expectations.\\n\\n### Recommendations from Sponsor's Perspective\\n\\n1. **Targeted Audience Selection:** Focus on a specific niche within the expected footfall range to maximize impact. This targeted approach can lead to higher conversion rates, making your investment more effective.\\n\\n2. **Event Enhancement and Diversification:** Consider adding activities or content that appeal to a broader audience. This could include workshops, masterclasses, exhibitions (if relevant), etc., to increase potential interest among different segments of the public.\\n\\n3. **Ticket Pricing Strategy Adjustment:** Review the ticket pricing strategy. While there might be some flexibility, reducing prices slightly could encourage more people to attend, making the event feel less niche and potentially increasing its reach and impact.\\n\\n4. **Engagement Strategies:** Develop strategies for deeper engagement with attendees before, during, and after the event. This can include interactive websites, social media contests, email newsletters that offer exclusive content or updates about the event, etc. The goal is to create a sense of community around the concert, encouraging loyalty and word-of-mouth promotion.\\n\\n5. **Media Sponsorship Maximization:** Utilize the media sponsorship to its fullest potential by ensuring the sponsor's brand is well-represented across all promotional materials (online, offline) and that their media channels are fully leveraged for coverage leading up to and during the event.\\n\\n6. **Measuring ROI Effectively:** It's crucial to establish a solid system for tracking engagement and conversion metrics from the sponsorship investment. This will allow you to refine strategies based on actual data rather than assumptions or estimates, maximizing your ROI over time.\\n\\n7. **Potential Revenue Streams:** Consider diversifying revenue streams beyond ticket sales. This could include merchandise sales, product placements within event activities, offering exclusive experiences (VIP packages) for a premium fee, etc. Diversification can help offset costs and improve the overall financial health of the event from a sponsor's perspective.\\n\\nBy implementing these strategies, it may be possible to improve the event's reach, engagement levels, and ultimately its ROI, making the ₹20,000 investment more effective for the sponsor.\"}"

    # Convert string to a Python dictionary
    data = ast.literal_eval(original_str)

    # Parse llama_output into structured data
    llama_output = data.pop('llama_output')

    # Extract analysis and recommendations
    analysis_start = llama_output.find("### Analysis")
    recommendations_start = llama_output.find("### Recommendations from Sponsor's Perspective")

    # Split into sections
    analysis_str = llama_output[analysis_start:recommendations_start].strip()
    recommendations_str = llama_output[recommendations_start:].strip()

    # Process Analysis
    analysis_lines = analysis_str.split("\n\n- ")
    analysis_lines[0] = analysis_lines[0].replace("**Expected Footfall:**", "").strip()

    analysis = []
    for line in analysis_lines[1:]:
        title, content = line.split(":", 1)
        analysis.append({
            "title": title.strip(),
            "content": content.strip()
        })

    # Process Recommendations
    recommendations_lines = recommendations_str.split("\n\n")
    recommendations = []
    for line in recommendations_lines[1:]:
        title, content = line.split(": ", 1)
        recommendations.append({
            "title": title.strip(),
            "content": content.strip()
        })

    # Construct the final JSON
    converted_data = {
        "task_id": "764fde05-c1b9-49b9-8487-83791b45b87f",
        "event_duration": "3",
        "event_type": "Concert",
        "expected_max_footfall": "500",
        "expected_min_footfall": "100",
        "prediction_revenue": data['prediction_revenue'],
        "roi": data['roi'],
        "roi_category": data['roi_category'],
        "scaled_roi": data['scaled_roi'],
        "sponsor_cost": "20000.00",
        "sponsor_type": "Media Sponsorship",
        "ticket_price": "150.00",
        "llama_output": {
            "title": "Based on the provided event details, here are some analysis and recommendations from a sponsor's perspective to potentially improve Return On Investment (ROI)",
            "Analysis": analysis,
            "Recommendations from Sponsor's Perspective": recommendations,
            "summary": "Based on the provided event details, here are some analysis and recommendations from a sponsor's perspective to potentially improve Return On Investment (ROI):"
        }
    }

    return jsonify(converted_data)

if __name__ == '__main__':
    app.run(debug=True)