"""
LAB 1: What is RAG? — Side-by-Side Comparison
================================================
Plain LLM vs RAG-powered LLM using Abacus.AI RouteLLM.
Deploy on HuggingFace Spaces with Gradio.
"""

import os
import gradio as gr
from openai import OpenAI

# Abacus.AI RouteLLM client (drop-in replacement for OpenAI)
client = OpenAI(
    base_url="https://routellm.abacus.ai/v1",
    api_key=os.getenv("ABACUS_API_KEY"),
)
MODEL = "gpt-4o-mini"

# Simulated private knowledge base
KNOWLEDGE_BASE = """
PavanCoaching AI Bootcamp starts on June 1, 2025.
The fee is ₹15,000 for the full course.
The course covers Python, ML, LLMs, RAG, and Agents.
Instructor: Pavan Kumar. Contact: pavan@pavancoaching.org
Batch size is limited to 30 students.
"""

def plain_llm(question: str) -> str:
    """Ask the LLM WITHOUT any context."""
    try:
        response = client.chat.completions.create(
            model=MODEL,
            messages=[{"role": "user", "content": question}],
        )
        return response.choices[0].message.content
    except Exception as e:
        return f"❌ Error: {e}"

def rag_llm(question: str) -> str:
    """Ask the LLM WITH retrieved context."""
    try:
        response = client.chat.completions.create(
            model=MODEL,
            messages=[
                {
                    "role": "system",
                    "content": (
                        "Answer ONLY using the context below.\n"
                        "If the answer is not in the context, say 'I don't know.'\n\n"
                        f"Context:\n{KNOWLEDGE_BASE}"
                    ),
                },
                {"role": "user", "content": question},
            ],
            temperature=0.1,
        )
        return response.choices[0].message.content
    except Exception as e:
        return f"❌ Error: {e}"

with gr.Blocks(title="Lab 1: RAG vs Plain LLM", theme=gr.themes.Soft()) as demo:
    gr.Markdown("## 🧪 Lab 1: Plain LLM vs RAG-Powered LLM")
    question = gr.Textbox(label="Your Question", value="When does the AI Bootcamp start?")
    btn = gr.Button("🔍 Compare", variant="primary")
    with gr.Row():
        out_plain = gr.Textbox(label="❌ Plain LLM (no context)", lines=8)
        out_rag = gr.Textbox(label="✅ RAG LLM (with context)", lines=8)
    btn.click(fn=lambda q: (plain_llm(q), rag_llm(q)), inputs=question, outputs=[out_plain, out_rag])

demo.launch()