app/examples/aws_storage_example.py

#!/usr/bin/env python3
"""
Example script demonstrating how to use the AWS S3 storage utilities.
"""

import os
from dotenv import load_dotenv
from app.utils.aws_storage import AWSS3Storage

def aws_storage_example():
    """
    Example of using the AWSS3Storage class to save and retrieve pitch data.
    """
    # Load environment variables
    load_dotenv()
    
    # Check if AWS credentials are configured
    if not all([
        os.getenv('AWS_ACCESS_KEY_ID'),
        os.getenv('AWS_SECRET_ACCESS_KEY'),
        os.getenv('AWS_S3_BUCKET_NAME')
    ]):
        print("AWS credentials not properly configured. Example will not work.")
        return
    
    # Initialize S3 storage
    s3_storage = AWSS3Storage()
    
    # Example pitch data
    example_script_id = f"example_pitch_{os.urandom(4).hex()}"
    example_elevator_pitch = "We're revolutionizing logistics with AI-powered optimization."
    example_full_pitch = """
    Our company, LogiTech AI, is addressing the critical inefficiencies in global supply chains.
    
    The Problem:
    Current logistics solutions fail to adapt in real-time to changing conditions, resulting in delays, wasted resources, and environmental impact.
    
    Our Solution:
    Our platform uses machine learning to dynamically optimize shipping routes and packaging in real-time, responding to disruptions within minutes instead of hours.
    
    Market Opportunity:
    The global logistics optimization market is projected to reach $30B by 2026, growing at 16% CAGR.
    
    Traction:
    We've already partnered with 3 Fortune 500 retailers for pilot programs, reducing their shipping costs by an average of 23%.
    
    Team:
    Our founding team combines 25+ years of logistics experience with cutting-edge AI expertise from MIT and Stanford.
    
    We're seeking $2M in funding to scale our technology and expand our customer base in the e-commerce sector.
    """
    
    # Example competitors and market insights
    example_competitors = """
    ## Competitor Analysis
    
    ### OptimizeShip
    A logistics optimization platform focused on route planning.
    
    **Strengths:**
    - Strong route optimization algorithms
    - Established market presence
    
    **Weaknesses:**
    - Lacks real-time adaptation
    - No packaging optimization
    
    ### PackTech
    Specializes in packaging optimization for shipping.
    
    **Strengths:**
    - Deep expertise in packaging materials
    - Integration with major e-commerce platforms
    
    **Weaknesses:**
    - No route optimization capabilities
    - Limited AI implementation
    """
    
    example_market_insights = """
    ## Market Insights
    
    The logistics optimization market is experiencing rapid growth due to:
    
    1. Increasing e-commerce sales globally
    2. Rising shipping costs and supply chain disruptions
    3. Growing emphasis on sustainability in shipping
    4. Advancements in AI and machine learning technologies
    
    Major trends include:
    - Integration of IoT devices for real-time tracking
    - Demand for environmentally-friendly shipping solutions
    - Consolidation among logistics technology providers
    """
    
    print("=== AWS S3 Storage Example ===")
    print(f"Using bucket: {s3_storage.bucket_name}")
    print(f"Example script ID: {example_script_id}")
    
    # Step 1: Save pitch data
    print("\n--- Step 1: Saving pitch data ---")
    save_result = s3_storage.save_pitch_data(
        script_id=example_script_id,
        elevator_pitch=example_elevator_pitch,
        full_pitch=example_full_pitch,
        competitors_data=example_competitors,
        market_insights=example_market_insights
    )
    
    if save_result["success"]:
        print(f"✅ Successfully saved pitch data")
        print(f"S3 path: {save_result['s3_path']}")
    else:
        print(f"❌ Failed to save pitch data: {save_result['message']}")
        return
    
    # Step 2: Retrieve pitch data
    print("\n--- Step 2: Retrieving pitch data ---")
    retrieve_result = s3_storage.get_pitch_data(example_script_id)
    
    if retrieve_result["success"]:
        pitch_data = retrieve_result["data"]
        print("✅ Successfully retrieved pitch data")
        print(f"Retrieved script ID: {pitch_data['script_id']}")
        print(f"Created at: {pitch_data['created_at']}")
        print("\nElevator pitch preview:")
        print(f""{pitch_data['elevator_pitch']}"")
    else:
        print(f"❌ Failed to retrieve pitch data: {retrieve_result['message']}")
    
    # Step 3: List pitches
    print("\n--- Step 3: Listing pitches ---")
    list_result = s3_storage.list_pitches(limit=5)
    
    if list_result["success"]:
        pitches = list_result["pitches"]
        print(f"✅ Successfully listed pitches. Total count: {list_result['count']}")
        
        if list_result["count"] > 0:
            print("\nRecent pitches:")
            for i, pitch in enumerate(pitches[:5], 1):  # Show up to 5 pitches
                print(f"{i}. {pitch['script_id']} (modified: {pitch['last_modified'].split('T')[0]})")
    else:
        print(f"❌ Failed to list pitches: {list_result['message']}")
    
    print("\n=== Example Complete ===")
    print(f"You can view this example pitch using the command:")
    print(f"python -m app.scripts_storage.get_pitch_details {example_script_id}")

if __name__ == "__main__":
    aws_storage_example()