app.py

# app.py
import gradio as gr
from search import search_google
from scraper import scrape_url
from rag import VectorStore
from llm import generate_answer

vs = VectorStore()

def ask_agent(question):
    urls = search_google(question, num_results=3)
    texts = [scrape_url(url) for url in urls]
    vs.add_texts(texts)
    relevant = vs.retrieve(question, top_k=2)
    context = "\n\n".join(relevant)
    answer = generate_answer(context, question)
    return f"## 🧠 Answer\n\n{answer}\n\n---\n### 🔗 Sources\n" + "\n".join(f"- [{url}]({url})" for url in urls)

with gr.Blocks(theme=gr.themes.Soft(primary_hue="violet", secondary_hue="blue")) as demo:
    gr.Markdown("""
    # 🔍 **AI Web RAG Agent**  
    _Ask anything, I'll search, scrape & answer in real time!_
    """)

    with gr.Column(scale=1):
        question = gr.Textbox(
            label="💡 Your question",
            placeholder="e.g., Find cheapest flights Kanpur to Mumbai on 30 July",
            show_label=True,
            scale=1
        )

        btn = gr.Button("🚀 Ask")

        output = gr.Markdown("🤖 *Your answer will appear here...*")

        # Examples help new users
        gr.Examples(
            examples=[
                "Best laptop under 50,000 INR",
                "Latest news about ISRO moon mission",
                "What are some tourist places near Mumbai"
            ],
            inputs=[question]
        )

    btn.click(fn=ask_agent, inputs=question, outputs=output)

demo.launch()