Use Cases & Examples

from brainpredict_innovation import BrainPredictInnovation

client = BrainPredictInnovation(api_key='YOUR_API_KEY')

# Get all unscored ideas from IdeaScale
ideas = client.get_ideas(status='submitted', scored=False, limit=500)

# Score all ideas
scored_ideas = []
for idea in ideas:
    score = client.score_idea(
        idea_id=idea['id'],
        title=idea['title'],
        description=idea['description'],
        category=idea['category'],
        submitter_profile=idea['submitter'],
        market_data=idea['market_research']
    )
    scored_ideas.append(score)

# Sort by total score (highest first)
scored_ideas.sort(key=lambda x: x['total_score'], reverse=True)

# Print top 20 ideas
print("Top 20 Innovation Ideas:")
for i, idea in enumerate(scored_ideas[:20], 1):
    print(f"{i}. {idea['title']}")
    print(f"   Total Score: {idea['total_score']}/100")
    print(f"   Feasibility: {idea['feasibility_score']}/100")
    print(f"   Impact: {idea['impact_score']}/100")
    print(f"   Novelty: {idea['novelty_score']}/100")
    print(f"   Strategic Alignment: {idea['alignment_score']}/100")
    print(f"   Recommendation: {idea['recommendation']}")
    print()

# Calculate statistics
avg_score = sum(i['total_score'] for i in scored_ideas) / len(scored_ideas)
high_potential = [i for i in scored_ideas if i['total_score'] >= 80]

print(f"\nStatistics:")
print(f"Total ideas scored: {len(scored_ideas)}")
print(f"Average score: {avg_score:.1f}/100")
print(f"High-potential ideas (≥80): {len(high_potential)}")
print(f"Recommended for further evaluation: {len([i for i in scored_ideas if i['recommendation'] == 'Proceed'])}")