Upload 2 files

app.py

@@ -1,112 +1,58 @@
-from langchain_community.document_loaders import TextLoader
-import os 
-
-loaders = []
-folder_path = "Data"
-
-for i in range(12):
-  file_path = os.path.join(folder_path,"{}.txt".format(i))
-  loaders.append(TextLoader(file_path))
-  
-docs = []
-for loader in loaders:
-    docs.extend(loader.load())
-
-from langchain.vectorstores import Chroma
-from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-embeddings = HuggingFaceInferenceAPIEmbeddings(
-    api_key=HF_TOKEN, model_name="sentence-transformers/all-mpnet-base-v2"
-)
-
-vectordb = Chroma.from_documents(
-    documents=docs,
-    embedding=embeddings
-)
-
-from langchain_community.llms import HuggingFaceHub
-
-llm = HuggingFaceHub(
-    repo_id="google/gemma-1.1-2b-it",
-    task="text-generation",
-    model_kwargs={
-        "max_new_tokens": 7000,
-        "top_k": 5,
-        "temperature": 0.1,
-        "repetition_penalty": 1.03,
-    },
-    huggingfacehub_api_token = HF_TOKEN
-)
-
-from langchain.prompts import PromptTemplate
-
-template = """You are a Chatbot at a Restaurant. Help the customer pick the right dish to order. The items in the context are dishes. The field below the item is the cost of the dish. About is the description of the dish. Use the context below to answe the questions 
-{context}
-Question: {question}
-Helpful Answer:"""
-QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template,)
-
-from langchain.memory import ConversationBufferMemory
-memory = ConversationBufferMemory(
-    memory_key="chat_history",
-    return_messages=True
-)
-
-from langchain.chains import ConversationalRetrievalChain
-
-retriever = vectordb.as_retriever()
-qa = ConversationalRetrievalChain.from_llm(
-    llm,
-    retriever=retriever,
-    memory=memory,
-)
-
-from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-
-contextualize_q_system_prompt = """Given a chat history and the latest user question \
-which might reference context in the chat history, formulate a standalone question \
-which can be understood without the chat history. Do NOT answer the question, \
-just reformulate it if needed and otherwise return it as is."""
-contextualize_q_prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", contextualize_q_system_prompt),
-        MessagesPlaceholder(variable_name="chat_history"),
-        ("human", "{question}"),
-    ]
-)
-contextualize_q_chain = contextualize_q_prompt | llm | StrOutputParser()
-
-def contextualized_question(input: dict):
-    if input.get("chat_history"):
-        return contextualize_q_chain
-    else:
-        return input["question"]
-
-
-rag_chain = (
-    RunnablePassthrough.assign(
-        context=contextualized_question | retriever 
-    )
-    | QA_CHAIN_PROMPT
-    | llm
-)
-
-
-from langchain_core.messages import AIMessage, HumanMessage
-
-os.environ["LANGCHAIN_WANDB_TRACING"] = "true"
-os.environ["WANDB_PROJECT"] = "Restaurant_ChatBot"
-
-print("Welcome to the Restaurant. How can I help you today?")
-chat_history = []
-
-def predict(message):
-  ai_msg = rag_chain.invoke({"question": message, "chat_history": chat_history})
-  idx = ai_msg.find("Answer")
-  chat_history.extend([HumanMessage(content=message), ai_msg])
-
-  return ai_msg[idx:]
-
+from langchain_groq import ChatGroq
+
+llm = ChatGroq(
+    temperature=0,
+    groq_api_key = "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",
+    model_name="llama3-8b-8192"
+)
+
+from crewai import Agent, Task, Crew
+import os
+
+Code_Quality_agent = Agent(
+    role="Senior Software Engineer",
+    goal="Provide the best support quality assurance to the code written by the member in your team",
+    backstory="You work in a financial organization. The Goal is to identify bugs,"
+    " security should be a top concern. Look for common Look for common"
+    " vulnerabilities like SQL injection, cross-site scripting (XSS), "
+    "and insecure data handling practices.Ensure the code adheres to"
+    " secure coding standards established by the organization or "
+    "industry.Scrutinize how the code validates user input to prevent"
+    " malicious attacks.For code involving financial calculations"
+    " (e.g., interest rates, risk assessments), double-check the formulas"
+    " and logic for accuracy. Consider edge cases and ensure the code behave"
+    "s as intended under various scenarios.Verify that the code maintains data"
+    " integrity throughout processing. This includes checking for potential"
+    " data loss, corruption, or unauthorized access.Ensure the code complies"
+    " with relevant industry standards and regulations.",
+    verbose=True,
+    allow_delegation=False,
+    llm = llm
+    
+)
+
+Code_Review = Task(expected_output=(
+        "You would be given code as an input for the code review {code}. "
+        "Make sure to use everything you know to provide the best support possible."
+        "Provide clear and actionable feedback to the code author.Maintain a collaborative and respectful tone throughout the review process.Ensure the code complies with relevant industry standards and regulations."
+    ),
+    description=(
+        "You would be given a code as an input for the code review {code}."
+        "Make sure to use everything you know to provide the best support possible."
+        "You must strive to provide a complete and accurate response."
+    ),
+    llm = llm,
+    agent= Code_Quality_agent,
+)
+
+crew = Crew(
+    agents=[Code_Quality_agent],
+    tasks=[Code_Review],
+    verbose=2,
+)
+
+def predict(code):
+	input = {code:code}
+	result = crew.kickoff(inputs=inputs)
+	return result
+    

main.py

@@ -10,8 +10,8 @@ app = FastAPI()
 
 @app.get("/")
 async def root():
- return {"Restaurant ChatBot":"Version 1.0 'First Draft'"}
+ return {"Code Review Automation":"Version 1.0 'First Draft'"}
 
-@app.post("/Predict/")
-def read_user(input_json: str):
+@app.post("/AutomateReview/")
+def predict(input_json: str):
     return predict(input_json)