APP

app.py

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+import streamlit as st
+import pandas as pd
+import simplejson as json
+import re
+from autogen import UserProxyAgent, AssistantAgent
+
+# Streamlit UI Setup
+st.set_page_config(page_title="QA Test Case Generator", layout="wide")
+st.title("🧪 AI-Powered QA Test Case Generator")
+st.write("Enter a user story, and the AI will generate detailed test cases for you!")
+
+# User Input
+user_story = st.text_area("📜 Enter User Story:", "As a user, I want to log in so that I can access my account.")
+
+config_list = [
+    {
+        "model": "llama3.2",
+        "api_type": "ollama",
+        "stream": False,
+    }
+]
+
+# AutoGen Agents Setup
+qa_agent = AssistantAgent(
+    name="QA_Agent",
+    max_consecutive_auto_reply=1,
+    human_input_mode="NEVER",
+    llm_config={
+        "timeout": 600,
+        "cache_seed": 42,
+        "config_list": config_list,
+    }
+)
+
+user_proxy = UserProxyAgent(
+    name="User",
+    max_consecutive_auto_reply=1,
+    human_input_mode="NEVER",
+    code_execution_config=False
+)
+
+
+# Function to Generate Test Cases
+def generate_test_cases(story):
+    prompt = f"""
+    Generate **detailed** test cases for the following user story:
+    {story}
+
+    Include:
+    - Functional test cases
+    - Edge cases
+    - Negative test cases
+
+    Each test case should include all the following fields and cover the Functional, Regression, Integration, Performance, and Security aspects ,positive and negative test cases: 
+    - ID: Unique test case ID
+    - Summary: A brief description of the test objective.
+    - Test Type: Functional, Regression, Integration, Performance, Security, etc.
+    - Priority: High, Medium, or Low based on impact.
+    - Component: The feature or module under test.
+    - Step Description: Actions to be performed.
+    - Expected Result: The expected system behavior after executing the step.
+    - Actual Result: (To be recorded during test execution).
+    - Pass/Fail Status: (To be marked after execution).
+
+    Return the test cases in valid JSON Object format ONLY, inside triple backticks (```) and  no additional text:
+    
+    [
+        {{"ID": "", "Summary": "", "Test Type": "", "Priority": "", "Component": "", "Step Description": "", "Expected Result": "", "Actual Result": "", "Pass/Fail Status": ""}},
+        
+    ]
+    
+    """
+
+    # Initiating chat with the QA agent
+    response = user_proxy.initiate_chat(qa_agent, message=prompt)
+
+    # Extract JSON from AI response
+    return extract_json_from_response(response)
+
+
+# Function to Extract JSON from AI Response
+
+def extract_json_from_response(response):
+    raw_response = str(response.chat_history)
+    raw_response = raw_response.replace("\\n", "\n")
+   
+    
+    # Find all JSON code blocks inside triple backticks
+    matches = re.findall(r'```(?:json)?\n(.*?)\n```', raw_response, re.DOTALL)
+    
+    for match in matches:
+        match = match.strip()  # Normalize the JSON format
+        
+        # Clean up any unnecessary whitespace or newlines within the JSON string
+        cleaned_json = match.replace("\n", "").replace("\t", "")
+        
+        try:
+            parsed_json = json.loads(cleaned_json)
+           
+            return parsed_json  # Return the first valid JSON found
+        except json.JSONDecodeError:
+            print("❌ Failed to parse JSON:", cleaned_json)  # Print the failed block for debugging
+            continue  # Skip and try the next match
+    
+    print("❌ Failed to parse AI response as JSON.")
+    return None
+
+
+# Generate Button
+if st.button("🚀 Generate Test Cases"):
+    with st.spinner("Generating test cases..."):
+        test_cases = generate_test_cases(user_story)
+
+        if test_cases:
+            # Convert JSON data into a DataFrame
+            df = pd.DataFrame(test_cases)
+            st.dataframe(df)
+
+            # Download as CSV
+            csv = df.to_csv(index=False).encode('utf-8')
+            st.download_button("📥 Download Test Cases as CSV", data=csv, file_name="test_cases.csv", mime="text/csv")
+        else:
+            st.error("❌ Failed to generate test cases. Check AI response.")