Create app.py

app.py

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+import os
+import streamlit as st
+import traceback
+from langgraph.graph import StateGraph, START, END
+from langchain.schema import HumanMessage
+from langchain_groq import ChatGroq
+from langsmith import traceable
+from typing import TypedDict
+
+# Load API keys from environment variables
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+LANGSMITH_API_KEY = os.getenv("LANGSMITH_API_KEY")
+
+os.environ["LANGCHAIN_TRACING_V2"] = "true"
+os.environ["LANGCHAIN_API_KEY"] = LANGSMITH_API_KEY
+
+# Initialize Groq LLM
+llm = ChatGroq(groq_api_key=GROQ_API_KEY, model_name="mixtral-8x7b-32768")
+
+# Define State
+class State(TypedDict):
+    code_snippet: str
+    review_comments: str
+    suggestions: str
+    documentation: str
+    test_cases: str
+
+# Function to review the code
+@traceable(name="Code Review")
+def code_review(data):
+    code_snippet = data.get("code_snippet", "")
+    prompt = f"Review the following code and provide feedback:\n\n{code_snippet}"
+    response = llm([HumanMessage(content=prompt)])
+    return {"review_comments": response.content}
+
+# Function to generate improvement suggestions
+@traceable(name="Improvement Suggestions")
+def improvement_suggestions(data):
+    review_comments = data.get("review_comments", "")
+    prompt = f"Based on this review feedback, suggest improvements:\n\n{review_comments}"
+    response = llm([HumanMessage(content=prompt)])
+    return {"suggestions": response.content}
+
+# Function to generate documentation
+@traceable(name="Code Documentation Generator")
+def generate_documentation(data):
+    code_snippet = data.get("code_snippet", "")
+    prompt = f"Generate proper docstrings and inline comments for the following code:\n\n{code_snippet}"
+    response = llm([HumanMessage(content=prompt)])
+    return {"documentation": response.content}
+
+# Function to generate test cases
+@traceable(name="Test Case Suggestions")
+def generate_test_cases(data):
+    code_snippet = data.get("code_snippet", "")
+    prompt = f"Based on the given code, generate appropriate unit test cases:\n\n{code_snippet}"
+    response = llm([HumanMessage(content=prompt)])
+    return {"test_cases": response.content}
+
+# Create LangGraph Workflow
+def make_code_review_graph():
+    """Create a LangGraph workflow for automated code reviews"""
+    graph_workflow = StateGraph(State)
+    
+    graph_workflow.add_node("code_review", code_review)
+    graph_workflow.add_node("improvement_suggestions", improvement_suggestions)
+    graph_workflow.add_node("generate_documentation", generate_documentation)
+    graph_workflow.add_node("generate_test_cases", generate_test_cases)
+    
+    graph_workflow.add_edge(START, "code_review")
+    graph_workflow.add_edge("code_review", "improvement_suggestions")
+    graph_workflow.add_edge("improvement_suggestions", "generate_documentation")
+    graph_workflow.add_edge("generate_documentation", "generate_test_cases")
+    graph_workflow.add_edge("generate_test_cases", END)
+    
+    return graph_workflow.compile()
+
+# Streamlit UI
+st.title("🛠 AI-Powered Code Review with LangSmith Debugging")
+st.write("Automate your code reviews using Groq AI, LangGraph, and LangSmith.")
+
+code_snippet = st.text_area("Paste your code snippet for review:", height=200)
+
+if st.button("Review Code"):
+    if not code_snippet.strip():
+        st.warning("⚠️ Please enter a code snippet.")
+    else:
+        try:
+            review_agent = make_code_review_graph()
+            result = review_agent.invoke({"code_snippet": code_snippet})
+            
+            st.subheader("💡 Review Comments")
+            st.text_area("", result.get("review_comments", ""), height=150)
+            
+            st.subheader("🔧 Suggested Improvements")
+            st.text_area("", result.get("suggestions", ""), height=150)
+            
+            st.subheader("📖 Generated Documentation")
+            st.text_area("", result.get("documentation", ""), height=150)
+            
+            st.subheader("🧪 Suggested Test Cases")
+            st.text_area("", result.get("test_cases", ""), height=150)
+        except Exception as e:
+            st.error(f"⚠️ Error: {str(e)}\n{traceback.format_exc()}")