Create app.py

app.py

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+import gradio as gr
+from search import search_google
+from scraper.py import scrape_url
+from rag import VectorStore
+from llm import generate_answer
+
+vs = VectorStore()
+
+def ask_agent(question):
+    # Step 1: search
+    urls = search_google(question, num_results=3)
+    # Step 2: scrape
+    texts = [scrape_url(url) for url in urls]
+    # Step 3: embed + store
+    vs.add_texts(texts)
+    # Step 4: retrieve
+    relevant = vs.retrieve(question, top_k=2)
+    context = "\n\n".join(relevant)
+    # Step 5: generate answer
+    answer = generate_answer(context, question)
+    return f"### 🧠 Answer:\n{answer}\n\n\n### 🔗 Sources:\n" + "\n".join(urls)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🔍 AI Web RAG Agent\nAsk me anything; I'll search, scrape and answer!")
+    with gr.Row():
+        inp = gr.Textbox(label="Your question")
+    out = gr.Markdown()
+    btn = gr.Button("Ask")
+    btn.click(fn=ask_agent, inputs=inp, outputs=out)
+
+demo.launch()