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import os
import gradio as gr
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain_community.llms import HuggingFaceEndpoint
# Load Hugging Face API token
hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
# Load LLM with token
llm = HuggingFaceEndpoint(
repo_id="google/flan-t5-base",
huggingfacehub_api_token=hf_token,
model_kwargs={"temperature": 0.7, "max_length": 512}
)
summary_cache = ""
glossary_cache = ""
retriever_chain = None
def extract_text_and_summary(file):
global retriever_chain, summary_cache, glossary_cache
loader = PyPDFLoader(file.name)
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
splits = splitter.split_documents(docs)
full_text = "\n".join([doc.page_content for doc in splits])
embeddings = HuggingFaceEmbeddings()
db = FAISS.from_documents(splits, embeddings)
retriever = db.as_retriever()
retriever_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
summary_prompt = f"Summarize this legal document:\n{full_text[:1500]}"
glossary_prompt = f"Extract and define legal terms from the document:\n{full_text[:1500]}"
summary_cache = llm(summary_prompt)
glossary_cache = llm(glossary_prompt)
filename = "summary_output.txt"
with open(filename, "w", encoding="utf-8") as f:
f.write("=== Summary ===\n")
f.write(summary_cache + "\n\n")
f.write("=== Glossary ===\n")
f.write(glossary_cache + "\n")
return full_text, summary_cache, glossary_cache, filename
def answer_custom_question(question):
if retriever_chain:
return retriever_chain.run(question)
return "Please upload and process a document first."
with gr.Blocks() as demo:
gr.Markdown("## 🧾 Legal Document Summarizer Using LangChain")
with gr.Row():
file = gr.File(label="📁 Upload Legal PDF", file_types=[".pdf"])
process_btn = gr.Button("🔍 Extract & Summarize")
extracted_text = gr.Textbox(label="📄 Extracted Text", lines=10)
summary_output = gr.Textbox(label="📝 Summary", lines=5)
glossary_output = gr.Textbox(label="📘 Glossary", lines=5)
download_link = gr.File(label="⬇️ Download Summary")
with gr.Row():
user_question = gr.Textbox(label="❓ Ask a Custom Question")
custom_answer = gr.Textbox(label="🤖 AI Answer")
ask_btn = gr.Button("🧠 Get Answer")
process_btn.click(fn=extract_text_and_summary, inputs=file, outputs=[
extracted_text, summary_output, glossary_output, download_link
])
ask_btn.click(fn=answer_custom_question, inputs=user_question, outputs=custom_answer)
demo.launch()
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