f9a68ee335
- app.py: Gradio Interface wrapping BoschProductRAG.query() - Index built once at startup (not per-request) - Markdown output: answer + linked sources + latency - Example prompts included for quick demo - Verified: launches on :7860, returns correct answers Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
"""Gradio web UI for the Bosch HVAC Product Knowledge Bot.
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Run with: python app.py
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"""
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import os
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from dotenv import load_dotenv
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import gradio as gr
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from src.rag import BoschProductRAG
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load_dotenv()
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CATALOG_PATH = os.path.join(os.path.dirname(__file__), "src", "products_catalog.json")
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# Build the RAG index once at startup (not per-request).
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_rag = BoschProductRAG()
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_rag.build_index(_rag.load_products(CATALOG_PATH))
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def answer_question(question: str) -> str:
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if not question or not question.strip():
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return "Please enter a question."
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result = _rag.query(question)
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sources_md = "\n".join(
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f"- [{s['name']}]({s['url']})" if s["url"] else f"- {s['name']}"
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for s in result["sources"]
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)
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return (
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f"### Answer\n{result['answer']}\n\n"
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f"### Sources\n{sources_md or '_none retrieved_'}\n\n"
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f"---\n*Latency: {result['latency_ms']:.0f}ms*"
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)
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demo = gr.Interface(
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fn=answer_question,
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inputs=gr.Textbox(
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label="Ask about Bosch HVAC products",
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placeholder="e.g. Which heat pump is best for retrofits?",
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lines=2,
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),
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outputs=gr.Markdown(label="Response"),
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title="Bosch HVAC Product Knowledge Bot",
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description=(
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"RAG-powered assistant for Bosch HVAC sales & spec questions. "
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"Retrieval: FAISS + Google Gemini embeddings. Generation: Gemini Flash Lite."
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),
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examples=[
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"Which heat pump is best for retrofits?",
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"What's the most energy-efficient system you offer?",
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"Do you have a smart thermostat?",
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"What cooling systems use R32 refrigerant?",
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],
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)
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if __name__ == "__main__":
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demo.launch()
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