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| # app.py — Insurance Q&A (RAG) with Omantel logo from GitHub URL (centered top) | |
| # Minimal changes; logic preserved. Uses Pinecone + LlamaIndex + OpenAI. | |
| import os | |
| import logging | |
| import gradio as gr | |
| from pinecone import Pinecone, ServerlessSpec | |
| from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext, Settings | |
| from llama_index.vector_stores.pinecone import PineconeVectorStore | |
| from llama_index.embeddings.openai import OpenAIEmbedding | |
| from llama_index.llms.openai import OpenAI | |
| # ===== CONFIG ===== | |
| PINECONE_API_KEY = os.getenv("PINECONE_API_KEY") | |
| OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") | |
| PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "dds-insurance-index") | |
| PINECONE_REGION = os.getenv("PINECONE_REGION", "us-east-1") | |
| PINECONE_CLOUD = os.getenv("PINECONE_CLOUD", "aws") | |
| EMBED_MODEL = os.getenv("EMBED_MODEL", "text-embedding-3-small") # 1536-dim | |
| LLM_MODEL = os.getenv("LLM_MODEL", "gpt-4o-mini") | |
| DATA_DIR = "data" | |
| DEFAULT_TOP_K = 4 # internal similarity_top_k (no UI control) | |
| # Omantel logo (raw GitHub URL so it renders directly) | |
| LOGO_URL = "https://raw.githubusercontent.com/Decoding-Data-Science/Omantel/main/Omantel_Logo%20(1).png" | |
| if not PINECONE_API_KEY: | |
| raise RuntimeError("Missing PINECONE_API_KEY (set it in your Space → Settings → Variables).") | |
| if not OPENAI_API_KEY: | |
| raise RuntimeError("Missing OPENAI_API_KEY (set it in your Space → Settings → Variables).") | |
| logging.basicConfig(level=logging.INFO) | |
| log = logging.getLogger("dds-space") | |
| # ===== LlamaIndex / Pinecone ===== | |
| Settings.embed_model = OpenAIEmbedding(model=EMBED_MODEL, api_key=OPENAI_API_KEY) | |
| Settings.llm = OpenAI(model=LLM_MODEL, api_key=OPENAI_API_KEY) | |
| pc = Pinecone(api_key=PINECONE_API_KEY) | |
| def ensure_index(name: str, dim: int = 1536): | |
| names = [i["name"] for i in pc.list_indexes()] | |
| if name not in names: | |
| log.info(f"Creating Pinecone index '{name}' (dim={dim})...") | |
| pc.create_index( | |
| name=name, | |
| dimension=dim, | |
| metric="cosine", | |
| spec=ServerlessSpec(cloud=PINECONE_CLOUD, region=PINECONE_REGION), | |
| ) | |
| return pc.Index(name) | |
| pinecone_index = ensure_index(PINECONE_INDEX_NAME, dim=1536) | |
| vector_store = PineconeVectorStore(pinecone_index=pinecone_index) | |
| def bootstrap_index(): | |
| if not os.path.isdir(DATA_DIR): | |
| raise RuntimeError("No 'data/' directory found. Commit your documents to data/ in the Space repo.") | |
| log.info("Loading documents from ./data ...") | |
| docs = SimpleDirectoryReader(DATA_DIR).load_data() | |
| if not docs: | |
| raise RuntimeError("No documents found in data/. Add e.g., data/insurance.pdf") | |
| log.info(f"Docs loaded: {len(docs)}. Upserting into Pinecone…") | |
| storage_ctx = StorageContext.from_defaults(vector_store=vector_store) | |
| VectorStoreIndex.from_documents(docs, storage_context=storage_ctx, show_progress=True) | |
| log.info("Index upsert complete.") | |
| bootstrap_index() | |
| def answer(query: str) -> str: | |
| if not query or not query.strip(): | |
| return "Please enter a question (or select one from the FAQ list)." | |
| index = VectorStoreIndex.from_vector_store(vector_store) | |
| engine = index.as_query_engine(similarity_top_k=DEFAULT_TOP_K) | |
| resp = engine.query(query) | |
| return str(resp) | |
| FAQS = [ | |
| "", | |
| "What benefits are covered under the policy?", | |
| "How do I file a claim and what documents are required?", | |
| "What are the exclusions and limitations?", | |
| "Is pre-authorization needed for hospitalization?", | |
| "What is the reimbursement timeline?", | |
| "How are outpatient vs inpatient services handled?", | |
| "How can I check my network hospitals/clinics?", | |
| "What is the co-pay or deductible policy?", | |
| ] | |
| def use_faq(selected_faq: str, free_text: str): | |
| prompt = (selected_faq or "").strip() or (free_text or "").strip() | |
| if not prompt: | |
| return "", "Please select a FAQ or type your question." | |
| return prompt, answer(prompt) | |
| # ===== UI ===== | |
| CSS = """ | |
| .header { text-align:center; } | |
| .header img { max-height:80px; height:auto; } | |
| .title { text-align:center; font-weight:700; font-size:1.4rem; margin:6px 0 0 0; } | |
| .subnote { text-align:center; margin-top:-2px; opacity:0.8; } | |
| """ | |
| with gr.Blocks(css=CSS, theme=gr.themes.Soft()) as demo: | |
| # Centered logo + title | |
| gr.Markdown( | |
| f""" | |
| <div class="header"> | |
| <img src="{LOGO_URL}" alt="Omantel logo" /> | |
| </div> | |
| <h1 class="title">Omantel Insurance Q&A — RAG Assistant</h1> | |
| <p class="subnote">Ask about coverage, claims, exclusions, and more — powered by LlamaIndex + Pinecone</p> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### Ask from Frequently Asked Questions") | |
| faq = gr.Dropdown(choices=FAQS, value=FAQS[0], label="Select a common question") | |
| gr.Markdown("### Or type your question") | |
| user_q = gr.Textbox( | |
| label="Your question", | |
| placeholder="e.g., What is covered under outpatient benefits?", | |
| lines=2 | |
| ) | |
| ask_btn = gr.Button("Ask", variant="primary") | |
| with gr.Column(scale=1): | |
| chosen_prompt = gr.Textbox(label="Query sent", interactive=False) | |
| answer_box = gr.Markdown() | |
| ask_btn.click(use_faq, inputs=[faq, user_q], outputs=[chosen_prompt, answer_box]) | |
| if __name__ == "__main__": | |
| demo.launch() | |