Spaces:
Runtime error
Runtime error
File size: 5,061 Bytes
e5d1a79 6df6d81 e5d1a79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
from typing import Optional, Tuple
from os import getenv
import gradio as gr
from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
def get_openai_key(api_key: Optional[str]) -> str:
"""Use the supplied API key or fallback to the environment variable."""
if not api_key:
api_key = getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OpenAI API key is required.")
return api_key
# Define the schema for the Terraform configurations
class Terraform(BaseModel):
"""
A Terraform module with a list of resources, provider, and configuration files.
"""
main_tf: str = Field(..., description="The main Terraform configuration file.")
variables_tf: str = Field(..., description="The Terraform variables configuration file.")
outputs_tf: str = Field(..., description="The Terraform outputs configuration file.")
def generate(resources: str, provider: str, model: str, api_key: str = "") -> Tuple[str, str, str]:
try:
llm = ChatOpenAI(model=model, temperature=0.7, max_tokens=8192)
structured_llm = llm.with_structured_output(Terraform)
response = structured_llm.invoke(f"""You are an expert SRE assistant specialized in Terraform.
Generate a Terraform configuration for the following resources:
{resources} using the {provider} provider.
You always remember all of the necessary permissions and roles to configure for connecting the resources.
""")
return response.main_tf, response.variables_tf, response.outputs_tf
except Exception as e:
raise ValueError(f"Failed to generate Terraform configuration: {e}")
# Define the Gradio interface
def create_interface():
"""Interface for the Terraform Module Generator."""
with gr.Blocks() as demo:
# Title
gr.Markdown("## 🚀 Terraform Module Generator")
# Instructional Markdown Box
gr.Markdown("""
**Welcome to the Terraform Module Generator!**
This app assists you in generating Terraform configuration files based on your cloud provider and a natural language description of how your module should work.
**How to Use:**
1. **🔑 OpenAI API Key**: Enter your OpenAI API key to authenticate.
2. **📦 Module Description**: Describe the key resources and how they work together.
3. **🌐 Provider**: Select your cloud provider from the dropdown.
4. **🤖 Language Model**: Choose the OpenAI model to use for generation.
5. Click the **Generate Terraform Configuration** button to create your `.tf` files.
The generated configurations will be displayed in separate tabs for easy access. There's a copy button in the upper right.
""")
with gr.Row():
with gr.Column(scale=2):
api_key = gr.Textbox(
label="🔑 OpenAI API Key",
type="password",
placeholder="Enter your OpenAI API key",
)
with gr.Column(scale=1):
model = gr.Dropdown(
label="🤖 Language Model",
choices=["gpt-4o", "gpt-4o-mini"],
value="gpt-4o-mini", # Default selection
type="value",
)
with gr.Row():
resources = gr.Textbox(
label="📦 Module Description",
placeholder="Describe the resources and how they work together.",
lines=5,
scale=2,
)
provider = gr.Dropdown(
label="☁ Cloud Provider",
choices=["aws", "azure", "google"],
value="aws",
type="value",
scale=1,
)
with gr.Row():
generate_btn = gr.Button("⚙️ Generate Terraform Configuration")
with gr.Tab("📄 main.tf"):
main_tf_output = gr.Textbox(
label="📄 main.tf",
lines=20,
show_copy_button=True,
interactive=False
)
with gr.Tab("🔧 variables.tf"):
variables_tf_output = gr.Textbox(
label="🔧 variables.tf",
lines=20,
show_copy_button=True,
interactive=False
)
with gr.Tab("📤 outputs.tf"):
outputs_tf_output = gr.Textbox(
label="📤 outputs.tf",
lines=20,
show_copy_button=True,
interactive=False
)
# Define the button click event
generate_btn.click(
fn=generate,
inputs=[resources, provider, model, api_key],
outputs=[main_tf_output, variables_tf_output, outputs_tf_output]
)
return demo
# Launch the interface
if __name__ == "__main__":
demo = create_interface()
demo.launch()
|