app.py

import os
import shutil
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import DirectoryLoader
from langchain_openai import OpenAIEmbeddings
from langchain.vectorstores.chroma import Chroma
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
import gradio as gr


script_directory = os.path.dirname(os.path.abspath(__file__))
DATA_PATH = os.path.join(script_directory, "pdfs")
CHROMA_PATH = "chroma"
 	  
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
	  
PROMPT_TEMPLATE = """
Answer the question based only on the following context:
{context}
---
Answer the question based on the above context: {question}
"""

def load_documents():
    loader = DirectoryLoader(DATA_PATH, glob="*.pdf")
    documents = loader.load()
    return documents

def split_text(documents):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=300,
        chunk_overlap=100,
        length_function=len,
        add_start_index=True,
    )
    chunks = text_splitter.split_documents(documents)
    print(f"Split {len(documents)} documents into {len(chunks)} chunks.")
    return chunks

def save_to_chroma(chunks):
    # Clear out the database first.
    if os.path.exists(CHROMA_PATH):
        shutil.rmtree(CHROMA_PATH)

    embeddings = OpenAIEmbeddings()
    # Create a new DB from the documents.
    db = Chroma.from_documents(
        chunks, embeddings, persist_directory=CHROMA_PATH
   )
    db.persist()
    print(f"Saved {len(chunks)} chunks to {CHROMA_PATH}.")


def get_response(query_text):
  
    # Prepare the DB.
    embedding_function = OpenAIEmbeddings()
    db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embedding_function)
	  

    results = db.similarity_search_with_relevance_scores(query_text, k=4)
    if len(results) == 0 or results[0][1] < 0.7:
        print(f"Unable to find matching results.")
        return

    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
 	  
    context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results])
    prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
    prompt = prompt_template.format(context=context_text, question=query_text)
	  
    model = ChatOpenAI()
    response_text = model.predict(prompt)
    
    sources = [doc.metadata.get("source", None) for doc, _score in results]
    sources = list(dict.fromkeys(sources))
    formatted_response = f"Response: {response_text}\nSources: {sources}"
    return formatted_response
	  
def prepare():
    documents = load_documents()
    chunks = split_text(documents)
    save_to_chroma(chunks)
   
   
   	  

iface = gr.Interface(fn=get_response,
        inputs=gr.components.Textbox(lines=7, label="Enter your text"),
        outputs="text",
        title="UK Insurance Law AI Tool")
     


prepare()
iface.launch()