Update app.py

app.py

@@ -0,0 +1,147 @@
+import os
+import requests
+import json
+from bs4 import BeautifulSoup
+from textwrap import shorten
+
+import gradio as gr
+
+from langchain_core.documents import Document
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+# -----------------------
+# 1. SCRAPE K8S DOCS
+# -----------------------
+urls = {
+    "pods": "https://kubernetes.io/docs/concepts/workloads/pods/",
+    "deployments": "https://kubernetes.io/docs/concepts/workloads/controllers/deployment/",
+    "services": "https://kubernetes.io/docs/concepts/services-networking/service/",
+    "namespaces": "https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/",
+    "nodes": "https://kubernetes.io/docs/concepts/architecture/nodes/",
+    "statefulsets": "https://kubernetes.io/docs/concepts/workloads/controllers/statefulset/",
+    "rbac": "https://kubernetes.io/docs/reference/access-authn-authz/rbac/",
+    "persistent-volumes": "https://kubernetes.io/docs/concepts/storage/persistent-volumes/",
+    "ingress": "https://kubernetes.io/docs/concepts/services-networking/ingress/",
+    "autoscaling": "https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/"
+}
+
+def scrape_docs():
+    docs = []
+    for name, url in urls.items():
+        try:
+            r = requests.get(url, timeout=20)
+            soup = BeautifulSoup(r.text, "html.parser")
+            content = soup.find("div", class_="td-content")
+            if not content:
+                continue
+            text = content.get_text(separator="\n").strip()
+            docs.append(Document(page_content=text, metadata={"doc_id": name, "url": url}))
+        except Exception:
+            continue
+    return docs
+
+docs = scrape_docs()
+
+# -----------------------
+# 2. CHUNK + EMBED + VECTOR DB
+# -----------------------
+splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100)
+chunks = splitter.split_documents(docs)
+
+embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+vectordb = Chroma.from_documents(chunks, embedding)
+retriever = vectordb.as_retriever(
+    search_type="similarity_score_threshold",
+    search_kwargs={"k": 5, "score_threshold": 0.4}
+)
+
+# -----------------------
+# 3. RAG HELPERS
+# -----------------------
+def build_context_with_citations(query: str):
+    retrieved_docs = retriever.invoke(query)
+    context = ""
+    mapping = []
+
+    for i, d in enumerate(retrieved_docs, start=1):
+        label = f"[{i}]"
+        context += f"{label} {d.page_content[:1000]}\n\nSource: {d.metadata['url']}\n\n"
+        mapping.append({
+            "label": label,
+            "url": d.metadata["url"],
+            "doc": d.metadata["doc_id"],
+            "preview": shorten(d.page_content, width=200)
+        })
+    return context, mapping
+
+def build_prompt(query, context):
+    return f"""
+You are a Kubernetes expert.
+Use ONLY the context below.
+Add citations like [1][2] after each fact.
+If not found, say: 'Not in docs'.
+
+QUESTION:
+{query}
+
+CONTEXT:
+{context}
+""".strip()
+
+# -----------------------
+# 4. OPENROUTER LLM
+# -----------------------
+import requests as req
+
+OPENROUTER_API_KEY = os.environ.get("OPENROUTER_API_KEY", "")
+
+def call_llm(prompt: str) -> str:
+    if not OPENROUTER_API_KEY:
+        return "OpenRouter API key is not set. Please configure OPENROUTER_API_KEY in the Space settings."
+
+    url = "https://openrouter.ai/api/v1/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    data = {
+        "model": "meta-llama/llama-3.1-8b-instruct",
+        "messages": [
+            {"role": "system", "content": "You are a Kubernetes expert. Only use provided context."},
+            {"role": "user", "content": prompt}
+        ],
+        "temperature": 0.0
+    }
+    response = req.post(url, headers=headers, data=json.dumps(data))
+    out = response.json()
+    return out.get("choices", [{"message": {"content": "No response"}}])[0]["message"]["content"]
+
+def answer_question(query: str):
+    context, sources = build_context_with_citations(query)
+    prompt = build_prompt(query, context)
+    answer = call_llm(prompt)
+    return answer, sources
+
+# -----------------------
+# 5. GRADIO CHAT APP
+# -----------------------
+def chat_fn(message, history):
+    answer, sources = answer_question(message)
+    src_lines = [f"{s['label']} – {s['url']}" for s in sources]
+    sources_text = "\n".join(src_lines) if src_lines else "No sources found."
+    full_answer = f"{answer}\n\n---\nSources:\n{sources_text}"
+    return full_answer
+
+demo = gr.ChatInterface(
+    fn=chat_fn,
+    title="Kubernetes RAG Assistant",
+    description="Ask Kubernetes questions. Answers are grounded in official docs and include citations."
+)
+
+def main():
+    return demo
+
+if __name__ == "__main__":
+    demo.launch()