Upload backend.py

backend.py

@@ -0,0 +1,122 @@
+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 &lt; back to < and &gt; back to >)
+    text = html.unescape(text)
+    
+    # Use BeautifulSoup to handle HTML tags more comprehensively
+    soup = BeautifulSoup(text, 'html.parser')
+    
+    # Convert <br> 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=2048,  # 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 <br> 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'
+