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update app.py
Browse files
app.py
CHANGED
@@ -4,19 +4,22 @@ from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer
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import gradio as gr
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# Configure logging
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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#
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MODELS_TO_TRY = [
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"google/flan-t5-
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"
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"
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"t5-large"
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]
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def load_model():
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for model_name in MODELS_TO_TRY:
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try:
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logger.info(f"Attempting to load model: {model_name}")
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@@ -25,129 +28,90 @@ def load_model():
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Create
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generator = pipeline(
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"text2text-generation",
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model=model,
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tokenizer=tokenizer,
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max_length=
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num_return_sequences=1
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)
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logger.info(f"Successfully loaded model: {model_name}")
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return generator
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except Exception as
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logger.error(f"Failed to load model {model_name}: {
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logger.error("
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return None
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# Load
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generator = load_model()
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def generate_test_cases(method, url, headers, payload=""):
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try:
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#
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logger.info(f"
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logger.info(f"URL: {url}")
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logger.info(f"Headers: {headers}")
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logger.info(f"Payload: {payload}")
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# Validate inputs
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if not method or not url:
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return "Error: Method and URL are required"
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#
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try:
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headers_dict = json.loads(headers) if headers else {}
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payload_dict = json.loads(payload) if payload else {}
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except json.JSONDecodeError as
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return f"JSON Parsing Error: {
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#
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prompt = f"""
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Generate
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Test Case Scenario: API Endpoint Testing
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HTTP Method: {method}
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API
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Test Case Requirements:
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1. Happy Path Scenarios:
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- Successful request with valid inputs
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- Verify correct response status code
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- Validate response structure and content
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- Missing required headers
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- Out-of-range parameter values
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3. Edge Case Considerations:
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- Maximum/minimum input limits
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- Special character handling
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- Unicode and internationalization testing
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4. Performance and Security Checks:
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- Response time validation
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- Payload size limits
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- Basic security vulnerability checks
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Output Format:
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For each test case, provide:
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- Test Case ID
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- Description
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- Preconditions
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- Input Data
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- Expected Result
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- Actual Result Verification Steps
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"""
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#
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if generator is None:
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return "Error: No
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# Generate test cases
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generated_text = response[0]['generated_text']
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logger.info("Test cases generated successfully")
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logger.debug(f"Generated Text: {generated_text}")
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return generated_text
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return f"Error generating test cases: {generation_error}"
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except Exception as
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logger.error(f"
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return f"
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# Gradio Interface
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iface = gr.Interface(
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fn=generate_test_cases,
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inputs=[
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gr.Textbox(label="HTTP Method (GET, POST, etc.)", placeholder="e.g., GET
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gr.Textbox(label="API URL", placeholder="e.g., https://api.example.com/endpoint"),
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gr.Textbox(label="Headers (JSON format)", placeholder='e.g., {"Content-Type": "application/json"}'),
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gr.Textbox(label="Payload (JSON format)", placeholder='e.g., {"key": "value"}'),
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],
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outputs="text",
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title="
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description="
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)
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# Main execution
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if __name__ == "__main__":
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try:
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logger.info("
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iface.launch()
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except Exception as
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logger.error(f"Failed to launch Gradio interface: {
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import gradio as gr
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# Configure logging
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# Models to try
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MODELS_TO_TRY = [
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"google/flan-t5-xxl", # Powerful instruction-following model
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"bigscience/T0pp", # Optimized for zero-shot tasks
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"t5-large", # General-purpose text generation
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"google/flan-t5-large" # Lightweight instruction-tuned model
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]
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def load_model():
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"""
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Attempt to load a suitable model for text generation.
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"""
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for model_name in MODELS_TO_TRY:
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try:
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logger.info(f"Attempting to load model: {model_name}")
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Create the text generation pipeline
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generator = pipeline(
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"text2text-generation",
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model=model,
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tokenizer=tokenizer,
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max_length=512,
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num_return_sequences=1
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)
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logger.info(f"Successfully loaded model: {model_name}")
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return generator
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except Exception as e:
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logger.error(f"Failed to load model {model_name}: {e}")
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logger.error("All model attempts failed. No model loaded.")
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return None
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# Load the generator at startup
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generator = load_model()
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def generate_test_cases(method, url, headers, payload=""):
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"""
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Generate detailed API test cases using a language model.
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"""
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try:
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# Input validation and logging
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logger.info(f"Received inputs: Method={method}, URL={url}, Headers={headers}, Payload={payload}")
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if not method or not url:
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return "Error: HTTP Method and API URL are required inputs."
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# Parse headers and payload as JSON
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try:
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headers_dict = json.loads(headers) if headers.strip() else {}
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payload_dict = json.loads(payload) if payload.strip() else {}
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except json.JSONDecodeError as e:
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return f"JSON Parsing Error: {e}"
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# Prompt for the model
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prompt = f"""
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Generate comprehensive API test cases for the following:
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HTTP Method: {method}
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API URL: {url}
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Headers: {json.dumps(headers_dict, indent=2)}
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Payload: {json.dumps(payload_dict, indent=2)}
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Requirements:
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- Include Happy Path, Negative, and Edge Cases.
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- Provide validation steps and expected results.
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"""
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# Ensure model is loaded
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if generator is None:
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return "Error: No model is available for test case generation."
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# Generate test cases
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response = generator(prompt, max_length=500, num_return_sequences=1)
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generated_text = response[0]['generated_text']
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logger.info("Successfully generated test cases.")
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return generated_text
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except Exception as e:
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logger.error(f"Error during test case generation: {e}")
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return f"Error: {e}"
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# Gradio Interface
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iface = gr.Interface(
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fn=generate_test_cases,
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inputs=[
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gr.Textbox(label="HTTP Method (GET, POST, etc.)", placeholder="e.g., GET"),
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gr.Textbox(label="API URL", placeholder="e.g., https://api.example.com/endpoint"),
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gr.Textbox(label="Headers (JSON format)", placeholder='e.g., {"Content-Type": "application/json"}'),
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gr.Textbox(label="Payload (JSON format)", placeholder='e.g., {"key": "value"}'),
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],
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outputs="text",
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title="API Test Case Generator",
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description="Generate detailed API test cases using AI models."
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)
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# Main execution
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if __name__ == "__main__":
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try:
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logger.info("Launching Gradio interface...")
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iface.launch()
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except Exception as e:
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logger.error(f"Failed to launch Gradio interface: {e}")
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