import anthropic
import streamlit as st

client = anthropic.Anthropic()

SYSTEM_PROMPT = """You are DevOpsGPT, an expert DevOps and cloud engineering assistant.
Your expertise includes Docker, Kubernetes, Terraform, CI/CD, AWS, Azure, and monitoring.
Give practical, actionable answers with working commands when helpful.
Mention security implications for any configuration.
Keep answers concise but complete."""

def chat(messages: list) -> str:
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1000,
        system=SYSTEM_PROMPT,
        messages=messages
    )
    return response.content[0].text

st.title("DevOps Assistant")

if "messages" not in st.session_state:
    st.session_state.messages = []

for msg in st.session_state.messages:
    with st.chat_message(msg["role"]):
        st.markdown(msg["content"])

if prompt := st.chat_input("Ask about Docker, Kubernetes, AWS..."):
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(prompt)
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            response = chat(st.session_state.messages)
            st.markdown(response)
    st.session_state.messages.append({"role": "assistant", "content": response})

from anthropic import Anthropic
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores import Chroma

client = Anthropic()

class DocumentQA:
    def __init__(self):
        self.vectorstore = None
        self.embeddings = OllamaEmbeddings(model="nomic-embed-text")

    def load_document(self, path: str):
        docs = PyPDFLoader(path).load()
        chunks = RecursiveCharacterTextSplitter(
            chunk_size=500, chunk_overlap=50
        ).split_documents(docs)
        self.vectorstore = Chroma.from_documents(chunks, self.embeddings)
        return len(chunks)

    def answer(self, question: str) -> dict:
        relevant_docs = self.vectorstore.similarity_search(question, k=4)
        context = "

---

".join(d.page_content for d in relevant_docs)

        response = client.messages.create(
            model="claude-haiku-4-5-20251001",
            max_tokens=800,
            system="Answer questions based ONLY on the provided context. Say you do not have that information if it is not in the context.",
            messages=[{"role": "user", "content": f"Context:
{context}

Question: {question}"}]
        )

        return {
            "answer": response.content[0].text,
            "sources": [d.metadata.get("source", "Document") for d in relevant_docs]
        }

import anthropic, subprocess
client = anthropic.Anthropic()

tools = [
    {"name": "run_command",
     "description": "Run a bash diagnostic command and return output",
     "input_schema": {"type": "object",
         "properties": {"cmd": {"type": "string"}},
         "required": ["cmd"]}},
    {"name": "check_kubernetes",
     "description": "Get Kubernetes resource status",
     "input_schema": {"type": "object",
         "properties": {
             "resource": {"type": "string", "enum": ["pods","deployments","events"]},
             "namespace": {"type": "string", "default": "default"}},
         "required": ["resource"]}}
]

def execute_tool(name, inputs):
    if name == "run_command":
        r = subprocess.run(inputs["cmd"], shell=True,
                          capture_output=True, text=True, timeout=30)
        return r.stdout[:2000] + r.stderr[:500]
    if name == "check_kubernetes":
        r = subprocess.run(
            f"kubectl get {inputs['resource']} -n {inputs.get('namespace','default')}",
            shell=True, capture_output=True, text=True)
        return r.stdout + r.stderr

def agent(task: str, max_steps: int = 10) -> str:
    messages = [{"role": "user", "content": task}]
    for _ in range(max_steps):
        r = client.messages.create(model="claude-sonnet-4-6",
            max_tokens=4096, tools=tools, messages=messages,
            system="You are a DevOps expert. Use tools to investigate issues. Think step by step.")
        messages.append({"role": "assistant", "content": r.content})
        if r.stop_reason == "end_turn":
            return next(b.text for b in r.content if hasattr(b, "text"))
        if r.stop_reason == "tool_use":
            results = [{"type": "tool_result", "tool_use_id": b.id,
                        "content": execute_tool(b.name, b.input)}
                       for b in r.content if b.type == "tool_use"]
            messages.append({"role": "user", "content": results})
    return "Max steps reached"

print(agent("The payment service pods are crashing. Diagnose the issue."))