Initial commit: test case generator app

app.py

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+import os
+import gradio as gr
+from dotenv import load_dotenv
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms import HuggingFaceHub
+from langchain_core.documents import Document
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_community.tools.tavily_search.tool import TavilySearchResults
+# Load environment variables
+load_dotenv()
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HF_TOKEN")
+TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
+
+# Set API keys
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN
+os.environ["TAVILY_API_KEY"] = TAVILY_API_KEY
+
+# Prompt Template
+prompt_template = PromptTemplate(
+    input_variables=["context", "user_story"],
+    template="""You are a QA expert. Based on the context below and the given user story, write a detailed list of test cases.
+
+Context:
+{context}
+
+User Story:
+{user_story}
+
+Test Cases:"""
+)
+
+# Load knowledge from RAG (local file)
+def load_rag_knowledge():
+    with open("rag_knowledge_base.txt", "r", encoding="utf-8") as file:
+        content = file.read()
+    docs = [Document(page_content=content)]
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_store = FAISS.from_documents(docs, embeddings)
+    return vector_store.similarity_search("test case generation", k=1)
+
+# Tavily search
+def tavily_search(query):
+    search = TavilySearchResults(k=1)
+    results = search.run(query)
+    return results[0]['content'] if results else "No relevant results from Tavily."
+
+# LLM call with combined context
+def call_llm_with_context(context, user_story):
+    llm = HuggingFaceHub(
+        repo_id="mistralai/Mistral-7B-Instruct-v0.3",
+        model_kwargs={"temperature": 0.7, "max_new_tokens": 500}
+    )
+    chain = LLMChain(llm=llm, prompt=prompt_template)
+    return chain.run({"context": context, "user_story": user_story})
+
+# Generate test cases pipeline
+def generate_test_cases(user_story):
+    rag_docs = load_rag_knowledge()
+    rag_text = "\n".join([doc.page_content for doc in rag_docs])
+    tavily_text = tavily_search(user_story)
+    full_context = f"{rag_text}\n\n{tavily_text}"
+    test_cases = call_llm_with_context(full_context, user_story)
+    return rag_text.strip(), tavily_text.strip(), test_cases.strip()
+
+# Gradio handler
+def handle_generate(user_story):
+    rag, tavily, result = generate_test_cases(user_story)
+    return rag, tavily, result
+
+# ----------------- Gradio UI -----------------
+with gr.Blocks() as demo:
+    gr.Markdown("# 🧪 TechTales TestCaseGenerator using RAG + Tavily + Mistral + LangChain -  Developed by Pankaj Kumar")
+    gr.Markdown("🚀 Enter a user story below to generate test cases using your knowledge base and Tavily search.")
+
+    user_input = gr.Textbox(label="📝 Enter User Story", lines=4, placeholder="As a user, I want to...")
+
+    btn = gr.Button("🔍 Generate Test Cases")
+
+    rag_output = gr.Textbox(label="📚 Knowledge from RAG File", lines=8)
+    tavily_output = gr.Textbox(label="🌐 Knowledge from Tavily Search", lines=8)
+    result_output = gr.Textbox(label="✅ Final Test Cases", lines=12)
+
+    btn.click(
+        handle_generate,
+        inputs=[user_input],
+        outputs=[rag_output, tavily_output, result_output]
+    )
+
+demo.launch()

rag_knowledge_base.txt

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+### QA BEST PRACTICES
+
+- Always align test cases with the acceptance criteria of the user story.
+- Use boundary value analysis to catch edge case bugs.
+- Include negative test cases (e.g., invalid inputs, unauthorized access).
+- Consider accessibility and responsive testing for UI components.
+- Verify compliance requirements (GDPR, OWASP Top 10, etc.) when applicable.
+- Use exploratory testing to discover unexpected behavior in workflows.
+
+### COMMON TEST TYPES
+
+- Functional Testing: Verifies core functionality works as expected.
+- Regression Testing: Ensures existing functionality is unaffected by new changes.
+- Integration Testing: Verifies interfaces and interactions between modules/services.
+- Security Testing: Identifies vulnerabilities like XSS, CSRF, authentication flaws.
+- Performance Testing: Measures system speed, scalability, and reliability under load.
+- Usability Testing: Validates the ease of use and clarity of workflows.
+
+### TEST CASE EXAMPLES
+
+**Scenario: Login System**
+- Verify user can login with valid credentials.
+- Verify error is shown for incorrect password.
+- Check account lockout after 5 failed login attempts.
+- Test password reset flow with valid and invalid emails.
+- Ensure password input field is masked.
+- Attempt login from multiple devices concurrently.
+
+**Scenario: Shopping Cart**
+- Verify product is added to cart and cart total updates.
+- Check removal of an item updates cart total.
+- Add same item multiple times and validate quantity logic.
+- Test cart persistence after page refresh or relogin.
+- Simulate network delay and ensure cart sync is retained.
+
+**Scenario: API-based System**
+- Validate GET /users returns 200 with correct schema.
+- Ensure POST /create-user fails without required fields.
+- Test rate-limiting behavior under rapid API calls.
+- Check unauthorized access returns 401.
+
+### EDGE CASES
+
+- Inputs with special characters (e.g., %, $, <, >, `).
+- Extremely long strings (e.g., 500+ characters in a name field).
+- Input empty fields where mandatory.
+- Date/time edge values: Feb 29, 2038 year bug, etc.
+- Simulate backend unavailability (503, 504 errors).
+
+### TEST DESIGN TEMPLATES
+
+- Given [pre-condition], When [action], Then [expected result].
+- Use test data sets with both valid and invalid combinations.
+- Prioritize based on risk and usage frequency.
+
+### SECURITY COMPLIANCE
+
+- Enforce password policies: min length, complexity.
+- Ensure sensitive fields (password, token) are never logged.
+- Validate user input on both client and server sides.
+- CSRF protection for form submissions.
+- Encrypt data at rest and in transit.
+

requirements.txt

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+gradio
+requests
+python-dotenv
+pandas
+openpyxl
+tavily-python
+openai
+langchain
+langchain_community
+langchain_openai
+langchain_tavily
+huggingface_hub
+faiss-cpu
+transformers==4.36.2  # Compatible with Python < 3.10
+torch  # Add version if needed for compatibility: torch==2.0.1
+sentence-transformers
+