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from transformers import pipeline
import gradio as gr
# Load the model
model_name = "google/flan-t5-base" # Choose your desired model
generator = pipeline("text2text-generation", model=model_name)
# Function to generate test cases
def generate_test_cases(method, url, headers, payload = ""):
prompt = f"""
Generate comprehensive API test cases:
Method: {method}
URL: {url}
Headers: {headers}
Payload: {payload}
Include:
- Happy path
- Negative test cases
- Performance scenarios
- Boundary conditions
"""
try:
response = generator(prompt, max_length=500)
return response[0]['generated_text']
except Exception as e:
return f"Error generating test cases: {str(e)}"
# Create the Gradio interface
iface = gr.Interface(
fn=generate_test_cases,
inputs=[
gr.Textbox(label="HTTP Method (GET, POST, etc.)"),
gr.Textbox(label="API URL"),
gr.Textbox(label="Headers (JSON format)"),
gr.Textbox(label="Payload (JSON format)"),
],
outputs="text",
title="API Test Case Generator",
description="Generate structured test cases for APIs using Hugging Face models."
)
if __name__ == "__main__":
iface.launch()
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