Update app.py

app.py

@@ -1,31 +1,34 @@
-# 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
 
-# init vector store (in-memory)
-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
+    urls = search_google(question, num_results=5)
+    
+    if not urls:
+        return "❗ No search results found."
+
+    first_url = urls[0]
+    context = scrape_url(first_url)
+    
     answer = generate_answer(context, question)
-    return f"### 🧠 Answer:\n{answer}\n\n\n### 🔗 Sources:\n" + "\n".join(urls)
+    
+    # Show other high quality links as sources (skip first scraped one)
+    better_links = "\n".join([f- [{u}]({u})" for u in urls[1:]])
+    
+    return f"""### 🧠 **Answer**  
+{answer}
+
+✅ **Context scraped from:** [{first_url}]({first_url})
+
+📚 **Other useful links:**  
+{better_links if better_links else 'No other links'}
+"""
 
 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")
+    gr.Markdown("# 🔍 Dynamic RAG agent\nScrapes *one* top site, answers, and shows more trusted links.")
+    inp = gr.Textbox(label="Ask your question")
     out = gr.Markdown()
     btn = gr.Button("Ask")
     btn.click(fn=ask_agent, inputs=inp, outputs=out)