Automation and Content Generation

1.1 Oveview

In the Automation and Content Generation module, CapsureLabs leverages advanced language models, such as GPT (Generative Pre-trained Transformer), to automate content creation, streamline workflows, and enhance productivity. These tools generate text for various applications, including automated responses, marketing content, and personalized user interactions.

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1.2 Implementation Techniques: Integrating GPT APIs

1.2.1 Request Setup

• API Key Setup: Obtain and set up the OpenAI API key.

• Prompt Construction: Create tailored prompts to guide the model toward the desired content style.

1.2.2 Integration Code

import openai

# Initialize API with OpenAI key
openai.api_key = 'your_openai_api_key'

# Define a prompt for automated content generation
prompt = "Generate a welcoming message for new CapsureLabs users highlighting features like NFT creation and DeFi tools."

# API call to OpenAI's GPT model
response = openai.Completion.create(
  engine="text-davinci-003",
  prompt=prompt,
  max_tokens=100,
  temperature=0.7
)

# Extract and print generated content
print(response.choices[0].text.strip())

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1.3 Alternative Methods: Using Hugging Face Transformers

1.3.1 Setup Example with GPT-2

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

# Load model and tokenizer
model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Encode prompt text
input_text = "Create a description for a new CapsureLabs NFT collection featuring futuristic art."
inputs = tokenizer(input_text, return_tensors="pt")

# Generate text
outputs = model.generate(inputs["input_ids"], max_length=50, temperature=0.7)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)

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1.4 Automated Content Creation for Marketing

1.4.1 Content Generator Function

def generate_content(prompt, max_length=150, temperature=0.8):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=max_length,
        temperature=temperature
    )
    return response.choices[0].text.strip()

# Example usage
prompt = "Write a blog post introduction about the benefits of using DeFi tools on CapsureLabs."
content = generate_content(prompt)
print(content)