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import os
import gradio as gr
import pdfplumber
import re
from langchain.docstore.document import Document
from langchain.vectorstores import FAISS
from langchain.embeddings.base import Embeddings
from sklearn.feature_extraction.text import TfidfVectorizer
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
os.environ["OPENAI_API_KEY"] = os.environ["OPENROUTER_API_KEY"]
os.environ["OPENAI_API_BASE"] = "https://openrouter.ai/api/v1"
os.environ["OPENAI_API_HEADERS"] = '{"HTTP-Referer":"https://huggingface.co", "X-Title":"PDF-RAG"}'
#Section-aware PDF extractor
def extract_clean_sections(file_path):
with pdfplumber.open(file_path) as pdf:
full_text = ""
for page in pdf.pages:
text = page.extract_text()
if text:
text = re.sub(r'Systems Campus.*?Lahore', '', text)
text = re.sub(r'E-mail:.*?systemsltd\.com', '', text)
full_text += text + "\n"
pattern = r"(?<=\n)([A-Z][^\n]{3,50}):"
parts = re.split(pattern, full_text)
docs = []
for i in range(1, len(parts), 2):
title = parts[i].strip()
content = parts[i + 1].strip()
if len(content) > 20:
docs.append(Document(page_content=f"{title}:\n{content}", metadata={"section": title}))
return docs
#TF-IDF Embedding for RAG
class TfidfEmbedding(Embeddings):
def __init__(self):
self.vectorizer = TfidfVectorizer()
def fit(self, texts):
self.vectorizer.fit(texts)
def embed_documents(self, texts):
return self.vectorizer.transform(texts).toarray()
def embed_query(self, text):
return self.vectorizer.transform([text]).toarray()[0]
# prompt
TEMPLATE = """
You are a strict healthcare policy checker for Systems Ltd.
Always begin your answer clearly:
- Say "Yes, ..." if the claim is valid
- Say "No, ..." if the claim is not valid
- Say "Partially, ..." if it's conditionally allowed
Use the following policy information to support your answer.
{context}
Question: {question}
Answer:
"""
custom_prompt = PromptTemplate(template=TEMPLATE, input_variables=["context", "question"])
# Global state
retriever = None
qa_chain = None
# ✅ Process the PDF once when button is clicked
def load_policy():
global retriever, qa_chain
docs = extract_clean_sections("healthcare_policy.pdf")
texts = [doc.page_content for doc in docs]
embedder = TfidfEmbedding()
embedder.fit(texts)
vectordb = FAISS.from_texts(texts, embedder)
retriever = vectordb.as_retriever()
llm = ChatOpenAI(
model="tngtech/deepseek-r1t2-chimera:free",
base_url="https://openrouter.ai/api/v1",
api_key=os.getenv("OPENAI_API_KEY"),
default_headers={
"HTTP-Referer": "https://huggingface.co",
"X-Title": "PDF-RAG"
},
temperature=0.0
)
qa_chain_local = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=False,
chain_type_kwargs={"prompt": custom_prompt}
)
qa_chain = qa_chain_local
return "Policy loaded. You may now ask questions."
# ✅ Answer a claim question
def ask_policy_question(question):
if qa_chain is None:
return "Please click 'Ask about claim' to load the policy first."
try:
return qa_chain.run(question)
except Exception as e:
return f"Error: {str(e)}"
# ✅ Gradio UI
with gr.Blocks() as demo:
gr.Markdown("## SL HealthCare Claim Checker (RAG)")
load_btn = gr.Button("📥 Ask about claim (Load Policy)")
load_status = gr.Textbox(label="Status")
load_btn.click(fn=load_policy, outputs=load_status)
with gr.Row():
question = gr.Textbox(label="Enter your claim question")
ask_btn = gr.Button("Ask")
answer = gr.Textbox(label="Answer", lines=6)
ask_btn.click(fn=ask_policy_question, inputs=question, outputs=answer)
demo.launch()
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