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from fastapi import FastAPI, UploadFile, File
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
import os
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
import logging
app = FastAPI()
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')
load_dotenv()
genai.configure(api_key=os.getenv("AIzaSyD0GxR2J1JxGic807Cc89Jq6MB4aDJYgDc"))
def get_pdf_text(pdf_docs):
text = ""
for pdf in pdf_docs:
try:
pdf_reader = PdfReader(pdf.file)
for page in pdf_reader.pages:
text += page.extract_text()
except Exception as e:
logging.error(f"Error processing PDF file: {e}")
return text
def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=10000,
chunk_overlap=1000
)
chunks = text_splitter.split_text(text)
return chunks
def get_vector_store(text_chunks):
logging.info("Starting vector store creation")
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
vector_store = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
logging.info("FAISS vector store created")
faiss_index_dir = os.path.join(os.path.dirname(__file__), "faiss_index")
os.makedirs(faiss_index_dir, exist_ok=True)
vector_store.save_local(faiss_index_dir)
logging.info("FAISS vector store saved successfully.")
def get_conversation_chain():
prompt_template = """
Answer the question clear and precise. If not provided the context return the result as
"Sorry I dont know the answer", don't provide the wrong answer.
Context:\n {context}?\n
Question:\n{question}\n
Answer:
"""
model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
chain = load_qa_chain(model, chain_type='stuff', prompt=prompt)
return chain
def user_input(user_question):
logging.info("Processing user input")
faiss_index_dir = os.path.join(os.path.dirname(__file__), "faiss_index")
if not os.path.exists(faiss_index_dir):
return "Please upload and process PDF files before asking questions."
try:
new_db = FAISS.load_local(faiss_index_dir, GoogleGenerativeAIEmbeddings(model='models/embedding-001'), allow_dangerous_deserialization=True)
logging.info("FAISS vector store loaded successfully")
docs = new_db.similarity_search(user_question)
chain = get_conversation_chain()
response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
return response["output_text"]
except Exception as e:
logging.error(f"Error processing user input: {e}")
return f"Sorry, there was an error processing your request: {str(e)}. Please try again later."
@app.post("/upload_pdf/")
async def upload_pdf(pdf_docs: list[UploadFile] = File(...)):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
get_vector_store(text_chunks)
return {"message": "PDFs processed successfully. You can now ask questions."}
@app.get("/ask_question/")
async def ask_question(user_question: str):
response = user_input(user_question)
return {"response": response}
@app.get("/", response_class=HTMLResponse)
async def read_root():
return """
<html>
<head>
<title>Chat with PDFs</title>
</head>
<body>
<h1>Welcome to Chat with PDFs API</h1>
<p>Use POST /upload_pdf/ to upload PDF files.</p>
<p>Use GET /ask_question/ to ask questions from the PDFs you uploaded.</p>
</body>
</html>
""" |