Updated the code for automated Code peer review

.github/workflows/main.yml

@@ -0,0 +1,26 @@
+name: Sync to Hugging Face Space
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab 
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: false
+          
+      - name: Ignore large files
+        run : git filter-branch --index-filter 'git rm -rf --cached --ignore-unmatch "Automated Code"' HEAD
+
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN_BLOG_AI }}
+        run: git push --force https://neuralgeekroot:$HF_TOKEN@huggingface.co/spaces/neuralgeekroot/AutomatedCodeReview main
+       
+         

.gitignore

@@ -0,0 +1,3 @@
+automatedCodeReview/
+.env
+CodingPeerReview.zip

README.md

@@ -18,5 +18,3 @@ This Application automates code using an open-source LLM
 1. Clone repo
 2. Install dependencies: `pip install -r requirements.txt`
 3. Create env keys
-# Automated-Code-Peer-Review
-# Automated-Code-Peer-Review

app.py

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+import os
+from dotenv import load_dotenv
+from langgraph.graph import StateGraph, START, END
+from typing_extensions import TypedDict
+from enum import Enum
+from langchain_groq import ChatGroq
+from langchain_core.prompts import PromptTemplate
+import streamlit as st
+import langsmith
+
+# Load environment variables
+load_dotenv()
+os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
+os.environ['LANGCHAIN_API_KEY'] = os.getenv('LANGCHAIN_API_KEY')
+os.environ['LANGSMITH_TRACING_V2'] = 'true'
+os.environ['LANGCHAIN_PROJECT_NAME'] = os.getenv('LANGCHAIN_PROJECT_NAME')
+
+class Step(Enum):
+    INPUT = "input"
+    REVIEW = "review"
+    IMPROVISATION = "improvisation"
+    APPROVAL = "approval"
+    APPROVED = "approved"
+
+# Define the state structure
+class CodingState(TypedDict):
+    code: str
+    step: Step
+
+# Define prompts in a configuration dictionary
+PROMPTS = {
+    "coder": "Create a code as per the {code} provided",
+    "peer": "Review the code provided by the coder: {code}. "
+            "If correction is needed, return 'improvisation'. "
+            "If suggestions are needed, return 'approval'. "
+            "If the code is correct, return 'approved'.",
+    "manager": "Add necessary docstrings to the following code and approve it: {code}"
+}
+
+# Initialize the LLM
+llm = ChatGroq(model="qwen-2.5-32b")
+
+# Define the coder node
+def coder(state):
+    """Based on the input message the code is created by the coder."""
+    print("Coder Node: Generating code...")
+    try:
+        prompt = PromptTemplate.from_template(PROMPTS["coder"])
+        chain = prompt | llm
+        result = chain.invoke({'code': state['code']})
+        print(f"Coder Node: Generated code: {result.content}")
+        return {'code': result.content, 'step': Step.REVIEW}
+    except Exception as e:
+        print(f"Coder Node: Error generating code - {e}")
+        return {'code': state['code'], 'step': Step.IMPROVISATION} 
+
+# Define the peer node
+def peer(state):
+    """Reviewing the code provided by the coder and determining the next step."""
+    print("Peer Node: Reviewing code...")
+    try:
+        prompt = PromptTemplate.from_template(PROMPTS["peer"])
+        chain = prompt | llm
+        result = chain.invoke({'code': state['code']})
+
+        # Extract the decision step from result
+        decision = result.content.strip().lower()
+
+        # Validate decision
+        valid_decisions = [Step.IMPROVISATION.value, Step.APPROVAL.value, Step.APPROVED.value]
+        if decision not in valid_decisions:
+            print(f"Peer Node: Invalid decision '{decision}'. Defaulting to 'approval'.")
+            decision = Step.APPROVAL.value  # Default fallback
+
+        return {"code": state["code"], "step": Step(decision)}
+    except Exception as e:
+        print(f"Peer Node: Error reviewing code - {e}")
+        return {"code": state["code"], "step": Step.IMPROVISATION} 
+
+def manager(state):
+    """Add docstrings to the code and approve it."""
+    print("Manager Node: Adding docstrings and approving code...")
+    try:
+        prompt = PromptTemplate.from_template(PROMPTS["manager"])
+        chain = prompt | llm
+        result = chain.invoke({'code': state['code']})
+        print(f"Manager Node: Approved code: {result.content}")
+        return {'code': result.content, 'step': Step.APPROVED}
+    except Exception as e:
+        print(f"Manager Node: Error approving code - {e}")
+        return {'code': state['code'], 'step': Step.APPROVAL} 
+
+# Define the code validity function
+def code_validity(state):
+    """Determine the next step based on the current state."""
+    print(f"Code Validity: Current step: {state['step'].value}")
+    if state['step'] == Step.IMPROVISATION:
+        return "coder"
+    elif state['step'] == Step.APPROVAL:
+        return "manager"
+    elif state['step'] == Step.APPROVED:
+        return END
+
+# Build the workflow
+builder = StateGraph(CodingState)
+
+# Add nodes
+builder.add_node("coder", coder)
+builder.add_node("peer", peer)
+builder.add_node("manager", manager)
+
+# Add edges
+builder.add_edge(START, "coder")
+builder.add_edge("coder", "peer")
+builder.add_conditional_edges("peer", code_validity, {"coder": "coder", "manager": "manager", END: END})
+builder.add_edge("manager", END)
+
+# Compile the workflow
+workflow = builder.compile()
+
+# Streamlit frontend
+st.title("Automated Code Peer Review")
+st.write("Submit your code for an automated peer review using an open-source LLM.")
+
+# Text area for code input
+code = st.text_area("Paste your code here:", height=300)
+
+if st.button("Generate Code"):
+    if code.strip() == "":
+        st.error("Please paste some code to review.")
+    else:
+        with st.spinner("Generating review..."):
+            try:
+                # Invoke the workflow
+                result = workflow.invoke({"code": code, 'step': Step.INPUT})
+                st.success("Review Generated!")
+                st.write("### Code Review Feedback")
+                st.write(result['code'])
+
+            except Exception as e:
+                st.error(f"An error occurred: {e}")

requirements.txt

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+langchain
+langgraph
+langsmith
+streamlit
+langchain_groq
+dotenv
+langchain_community