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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Bank.pdf filter=lfs diff=lfs merge=lfs -text

Bank.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65b86fe7f0f01b1478f5516d1f90c6a66e465158bf0d997d9132a80f2c6d5439
+size 438003

app.py

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+#Hugging Face Spaces deployment file for a Gradio-based Policy & Claims Agent.
+
+from langchain_community.document_loaders import  PyPDFLoader
+from langchain_text_splitters import  RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+import os
+from pathlib import Path
+from langchain_huggingface import  HuggingFaceEmbeddings, HuggingFaceEndpoint, ChatHuggingFace
+from langchain_community.vectorstores import Chroma
+from langchain_classic.chains import create_retrieval_chain
+from langchain_classic.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+import getpass
+import gradio as gr
+import os
+
+PDF_PATH = Path("./Bank.pdf")
+os.environ["HF_TOKEN"] = HF_KEY
+os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_KEY
+
+def load_pdf():
+   # drive.mount('/content/drive')
+    #base_path = Path('/content/drive/MyDrive/GenerativeAI')
+    #file_path = base_path /'Bank.pdf'
+    file_path = str(PDF_PATH)
+    loader = PyPDFLoader(file_path)
+    pages = loader.load()
+    print(f"PDF loaed and returing page number {len(pages)} \n")
+    return pages
+
+
+def split_pages(pages):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=700,
+        chunk_overlap=50
+    )
+    chunks = text_splitter.split_documents(pages)
+    print(f"Created {len(chunks)} chunks")
+    return chunks
+
+def create_embedding():
+    embeddings = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/all-MiniLM-L6-v2",
+        model_kwargs={'device': 'cuda'}
+    )
+    return embeddings
+
+def create_vector(chunks, embeddings):
+    vector_db = Chroma.from_documents(documents=chunks,
+        embedding= embeddings,
+        persist_directory="./hf_cloud_db"
+    )
+
+    print(f"Created vectore DB using huggig face\n")
+    return vector_db
+
+def get_retriver(vector_db):
+    retriever = vector_db.as_retriever(search_type="similarity",
+        search_kwargs={"k": 5}
+    )
+    print(f"Retreiver created \n")
+    return retriever
+
+def create_model():
+    llm = HuggingFaceEndpoint(
+        repo_id="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+        task="text-generation",
+        max_new_tokens=500,
+        temperature=0.2,
+        do_sample=False,
+    )
+
+    chat_model = ChatHuggingFace(llm=llm)
+    print(f"Model created")
+    return chat_model
+
+def build_policy_prompt():
+    prompt = ChatPromptTemplate.from_template("""
+    You are a Policy & Claims agent.
+
+    Answer the user's question using ONLY the retrieved policy context.
+
+    Rules:
+    1. Do not guess.
+    2. Do not use outside knowledge.
+    3. If the answer is not found in the context, say:
+        "Answer not found in the provided policy document."
+    4. Keep the answer short and clear.
+    5. Mention source page number when available.
+
+    Question:
+    {input}
+
+    Context:
+    {context}
+    """)
+    return prompt
+
+def build_claim_prompt():
+    prompt = ChatPromptTemplate.from_template("""
+You are a Policy & Claims Copilot.
+
+You are doing a claim pre-check, not final claim approval.
+
+Use ONLY the retrieved policy context.
+
+Rules:
+1. Do not guess.
+2. Do not use outside knowledge.
+3. If evidence is missing, say:
+   "Unclear based on provided policy context."
+4. Give output in this format:
+
+Pre-check Result:
+- Likely Covered / Likely Not Covered / Unclear
+
+Reason:
+- Short explanation from the policy context
+
+Waiting Period / Limits:
+- Mention only if found
+
+Documents Needed:
+- Mention only if found
+
+Disclaimer:
+- This is only a pre-check, not final claim approval.
+
+User Claim Scenario:
+{input}
+
+Context:
+{context}
+""")
+    return prompt
+
+def rag_chaining(retriever, chat_model, prompt):
+    document_chain = create_stuff_documents_chain(chat_model, prompt)
+    rag_chain = create_retrieval_chain(retriever, document_chain)
+    return rag_chain
+
+def chat_function(message, history, mode):
+    if mode in ["Q&A", "Policy Q&A"]:
+        return ask_policy_question(message, APP["policy_chain"])
+    else:
+        return claim_precheck(message, APP["precheck_chain"])
+
+def format_sources(docs):
+    if not docs:
+        return "No sources found."
+
+    lines = []
+    seen = set()
+
+    for doc in docs:
+        page = doc.metadata.get("page", "N/A")
+        source = doc.metadata.get("source", "N/A")
+        key = (source, page)
+
+        if key not in seen:
+            seen.add(key)
+            lines.append(f"- File: {source}, Page: {page}")
+
+    return "\n".join(lines)
+
+def ask_policy_question(message, policy_chain):
+    result = policy_chain.invoke({"input": message})
+    answer = result.get("answer", "")
+    docs = result.get("context", [])
+    sources = format_sources(docs)
+
+    return f"""{answer}
+
+Sources:
+{sources}
+"""
+
+def claim_precheck(message, precheck_chain):
+    result = precheck_chain.invoke({"input": message})
+    answer = result.get("answer", "")
+    docs = result.get("context", [])
+    sources = format_sources(docs)
+
+    return f"""{answer}
+
+Sources:
+{sources}
+"""
+def launch_ui():
+    mode_input = gr.Radio(
+        choices=["Q&A", "Claim Pre-check"],
+        value="Q&A",
+        label="Mode"
+    )
+
+    demo = gr.ChatInterface(
+        fn=chat_function,
+        additional_inputs=[mode_input],
+        title="Policy & Claims Agent",
+        description=(
+            "Ask policy questions or run a basic claim pre-check using the uploaded PDF. "
+            "Responses are grounded in retrieved document chunks."
+        ),
+        examples=[
+            ["What is covered under hospitalization?", "Q&A"],
+            ["What documents are needed to submit a claim?", "Q&A"],
+            ["My policy started 4 months ago and I want to claim for a surgery. Is it likely covered?", "Claim Pre-check"],
+        ],
+        cache_examples=False
+    )
+
+    demo.launch(debug=False, share=True)
+
+
+def initialize_app():
+    pages = load_pdf()
+    chunks = split_pages(pages)
+    embeddings = create_embedding()
+    vector_db = create_vector(chunks, embeddings)
+    retriever = get_retriver(vector_db)
+    chat_model = create_model()
+
+    policy_prompt = build_policy_prompt()
+    precheck_prompt = build_claim_prompt()
+
+    policy_chain = rag_chaining(retriever, chat_model, policy_prompt)
+    precheck_chain = rag_chaining(retriever, chat_model, precheck_prompt)
+
+    return {
+        "policy_chain": policy_chain,
+        "precheck_chain": precheck_chain
+    }
+
+APP = initialize_app()
+demo = launch_ui()
+
+if __name__ == "__main__":
+    demo.launch()