import json import csv import io import requests import html # For escaping HTML characters from bs4 import BeautifulSoup from openai import OpenAI # Initialize OpenAI API with Nvidia's Mistral model client = OpenAI( base_url="https://integrate.api.nvidia.com/v1", api_key="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" ) def clean_test_case_output(text): """ Cleans the output to handle HTML characters and unwanted tags. """ # Unescape HTML entities (convert < back to < and > back to >) text = html.unescape(text) # Use BeautifulSoup to handle HTML tags more comprehensively soup = BeautifulSoup(text, 'html.parser') # Convert
tags to newlines and remove all other tags cleaned_text = soup.get_text(separator="\n").strip() return cleaned_text def generate_testcases(user_story): """ Generates advanced QA test cases based on a provided user story by interacting with Nvidia's Mistral model API. The prompt is refined for clarity, and the output is processed for better quality. :param user_story: A string representing the user story for which to generate test cases. :return: A list of test cases in the form of dictionaries. """ try: # Example few-shot learning prompt to guide the model completion = client.chat.completions.create( model="nv-mistralai/mistral-nemo-12b-instruct", # Using Mistral model messages=[ {"role": "user", "content": f"Generate QA test cases for the following user story: {user_story}"} ], temperature=0.06, # Further lowering temperature for precise and deterministic output top_p=0.5, # Prioritize high-probability tokens even more max_tokens=4096, # Increase max tokens to allow longer content stream=True # Streaming the response for faster retrieval ) # Initialize an empty string to accumulate the response test_cases_text = "" # Accumulate the response from the streaming chunks for chunk in completion: if chunk.choices[0].delta.content is not None: test_cases_text += chunk.choices[0].delta.content # Ensure the entire response is captured before cleaning if test_cases_text.strip() == "": return [{"test_case": "No test cases generated or output was empty."}] # Clean the output by unescaping HTML entities and replacing
tags test_cases_text = clean_test_case_output(test_cases_text) try: # Try to parse the output as JSON, assuming the model returns structured test cases test_cases = json.loads(test_cases_text) if isinstance(test_cases, list): return test_cases # Return structured test cases else: return [{"test_case": test_cases_text}] # Return as a list with the text wrapped in a dict except json.JSONDecodeError: # Fallback: return the raw text if JSON parsing fails return [{"test_case": test_cases_text}] except requests.exceptions.RequestException as e: print(f"API request failed: {str(e)}") return [] # Add options for multiple test case formats def export_test_cases(test_cases, format='json'): if not test_cases: return "No test cases to export." # Convert test cases (which are currently strings) into a structured format for CSV structured_test_cases = [{'Test Case': case} for case in test_cases] if format == 'json': # Improve JSON export to be line-by-line formatted return json.dumps(test_cases, indent=4, separators=(',', ': ')) # More readable format elif format == 'csv': # Check if test_cases is a list of dicts if isinstance(test_cases, list) and isinstance(test_cases[0], dict): output = io.StringIO() csv_writer = csv.DictWriter(output, fieldnames=test_cases[0].keys(), quoting=csv.QUOTE_ALL) csv_writer.writeheader() csv_writer.writerows(test_cases) return output.getvalue() else: raise ValueError("Test cases must be a list of dictionaries for CSV export.") # 2. Save test cases as a downloadable file def save_test_cases_as_file(test_cases, format='json'): if not test_cases: return "No test cases to save." if format == 'json': with open('test_cases.json', 'w') as f: json.dump(test_cases, f) elif format == 'csv': with open('test_cases.csv', 'w', newline='') as file: dict_writer = csv.DictWriter(file, fieldnames=test_cases[0].keys()) dict_writer.writeheader() dict_writer.writerows(test_cases) else: return f"Unsupported format: {format}" return f'{format} file saved'