Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+
+# Knowledge base
+documents = [
+    "Leave policy: Employees are entitled to 20 annual leaves per year.",
+    "Health insurance: The company provides full coverage for employees and partial coverage for dependents.",
+    "Working hours: Standard office hours are 9 AM to 6 PM, Monday to Friday.",
+    "Remote work: Employees may work from home 2 days per week with manager approval.",
+    "Promotions: Promotions are based on yearly performance reviews."
+]
+
+# Load embedding model
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+doc_embeddings = embedder.encode(documents, convert_to_numpy=True)
+
+# Create FAISS index
+dimension = doc_embeddings.shape[1]
+index = faiss.IndexFlatL2(dimension)
+index.add(doc_embeddings)
+
+# RAG function
+def rag_chat(message, history):
+    query_embedding = embedder.encode([message], convert_to_numpy=True)
+    D, I = index.search(query_embedding, k=1)
+    best_doc = documents[I[0][0]]
+    response = f"📘 Policy Reference: {best_doc}"
+    return response
+
+# Gradio UI
+demo = gr.ChatInterface(
+    fn=rag_chat,
+    title="💡 WellCare AI - HR Assistant",
+    description="Ask me anything about leave, health insurance, or company policy.",
+    theme="soft"
+)
+
+if __name__ == "__main__":
+    demo.launch()