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Update app.py
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app.py
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import gradio as gr
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from search import search_google
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from scraper import scrape_url
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vs = VectorStore()
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def ask_agent(question):
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# Step 1: search
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urls = search_google(question, num_results=3)
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# Step 2: scrape
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texts = [scrape_url(url) for url in urls]
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# Step 3: embed + store
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vs.add_texts(texts)
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# Step 4: retrieve
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relevant = vs.retrieve(question, top_k=2)
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context = "\n\n".join(relevant)
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# Step 5: generate answer
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answer = generate_answer(context, question)
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return f"
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with gr.Blocks() as demo:
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gr.Markdown("
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demo.launch()
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# app.py
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import gradio as gr
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from search import search_google
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from scraper import scrape_url
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vs = VectorStore()
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def ask_agent(question):
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urls = search_google(question, num_results=3)
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texts = [scrape_url(url) for url in urls]
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vs.add_texts(texts)
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relevant = vs.retrieve(question, top_k=2)
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context = "\n\n".join(relevant)
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answer = generate_answer(context, question)
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return f"## π§ Answer\n\n{answer}\n\n---\n### π Sources\n" + "\n".join(f"- [{url}]({url})" for url in urls)
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with gr.Blocks(theme=gr.themes.Soft(primary_hue="violet", secondary_hue="blue")) as demo:
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gr.Markdown("""
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# π **AI Web RAG Agent**
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_Ask anything, I'll search, scrape & answer in real time!_
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""")
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with gr.Column(scale=1):
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question = gr.Textbox(
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label="π‘ Your question",
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placeholder="e.g., Find cheapest flights Kanpur to Mumbai on 30 July",
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show_label=True,
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scale=1
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)
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btn = gr.Button("π Ask")
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output = gr.Markdown("π€ *Your answer will appear here...*")
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# Examples help new users
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gr.Examples(
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examples=[
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"Best laptop under 50,000 INR",
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"Latest news about ISRO moon mission",
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"What are some tourist places near Mumbai"
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],
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inputs=[question]
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)
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btn.click(fn=ask_agent, inputs=question, outputs=output)
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demo.launch()
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