Generate grant reports from project data

Generate report from structured project data

This shows how to structure your data and generate a comprehensive grant report. Adapt the data structure to match your project tracking.

from openai import OpenAI
import json

client = OpenAI()

# Your project data - pull this from your CRM/spreadsheets
project_data = {
    "funder": {
        "name": "Big Trust Foundation",
        "focus": "youth mental health",
        "grant_amount": 50000,
        "reporting_period": "Jan-Dec 2024"
    },
    "activities": [
        "Delivered 48 weekly group sessions for young people aged 16-25",
        "Ran 6 family support workshops",
        "Provided 1-1 mentoring for 15 high-risk individuals",
        "Created peer support network with 30 active members"
    ],
    "beneficiaries": {
        "direct": 78,
        "indirect": 120,
        "demographics": "68% female, 32% male, avg age 19, 45% from minority ethnic backgrounds"
    },
    "outcomes": {
        "wellbeing_improved": "72% showed improved wellbeing scores (WEMWBS)",
        "engaged_ongoing": "65% continued engagement after 3 months",
        "crisis_reduced": "85% reported reduced crisis episodes",
        "quotes": [
            "This group gave me somewhere I felt safe to talk",
            "My mentor helped me stay in college when I was going to drop out"
        ]
    },
    "budget": {
        "allocated": 50000,
        "spent": 48500,
        "breakdown": {
            "staff": 35000,
            "venue": 8000,
            "materials": 3500,
            "evaluation": 2000
        }
    },
    "challenges": [
        "Recruitment took longer than expected - needed 3 months not 1",
        "Some participants found group format difficult - adapted with more 1-1 support",
        "Covid cases in March disrupted 2 sessions - moved online"
    ],
    "learnings": [
        "Co-design with young people from the start led to better engagement",
        "Having both group and 1-1 options met different needs",
        "Peer support network became most valued element - will expand this"
    ],
    "next_steps": [
        "Continue peer network with community ownership",
        "Develop digital resources based on session content",
        "Expand to include 14-16 age group based on demand"
    ]
}

def generate_grant_report(project_data):
    prompt = f"""You are writing a grant report for {project_data['funder']['name']}.

They funded a {project_data['funder']['focus']} project with £{project_data['funder']['grant_amount']:,}
for the period {project_data['funder']['reporting_period']}.

Write a comprehensive grant report with these sections:

1. EXECUTIVE SUMMARY (200 words)
   Concise overview of what was delivered and achieved

2. PROJECT ACTIVITIES
   What was delivered:
   {json.dumps(project_data['activities'], indent=2)}

3. BENEFICIARIES REACHED
   Who we worked with:
   - Direct beneficiaries: {project_data['beneficiaries']['direct']}
   - Indirect beneficiaries: {project_data['beneficiaries']['indirect']}
   - Demographics: {project_data['beneficiaries']['demographics']}

4. OUTCOMES ACHIEVED
   Impact we created:
   {json.dumps(project_data['outcomes'], indent=2)}

   Include the beneficiary quotes naturally in this section.

5. FINANCIAL REPORT
   Budget allocated: £{project_data['budget']['allocated']:,}
   Budget spent: £{project_data['budget']['spent']:,}
   Breakdown: {json.dumps(project_data['budget']['breakdown'], indent=2)}

   Explain the budget in prose, not just numbers.

6. CHALLENGES AND LEARNINGS
   What didn't go to plan and what we learned:
   Challenges: {json.dumps(project_data['challenges'], indent=2)}
   Learnings: {json.dumps(project_data['learnings'], indent=2)}

7. SUSTAINABILITY AND NEXT STEPS
   {json.dumps(project_data['next_steps'], indent=2)}

Writing style:
- Professional but warm and authentic
- Focus on real impact, not jargon
- Use active voice
- Be honest about challenges
- Show learning and adaptation
- Connect back to the funder's focus on {project_data['funder']['focus']}

Target length: 2000-2500 words"""

    response = client.chat.completions.create(
        model="gpt-4o",  # GPT-4 for longer, coherent reports
        messages=[{"role": "user", "content": prompt}],
        temperature=0.7  # Some creativity but not too much
    )

    return response.choices[0].message.content

# Generate report
print("Generating grant report...")
report = generate_grant_report(project_data)

# Save to file
with open("grant_report_draft.txt", "w") as f:
    f.write(report)

print("\nReport generated and saved to grant_report_draft.txt")
print(f"Word count: approximately {len(report.split())} words")
print("\nNext steps:")
print("1. Review all numbers for accuracy")
print("2. Add specific beneficiary stories")
print("3. Check against funder's requirements")
print("4. Have colleague review for clarity")
print("5. Format according to funder template")