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import os
import gradio as gr
from PyPDF2 import PdfReader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
from langchain_community.vectorstores import FAISS
from langchain_classic.chains.question_answering import load_qa_chain
from langchain_core.prompts import PromptTemplate
from dotenv import load_dotenv
load_dotenv()
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
INDEX_PATH = "faiss_index"
def get_pdf_text(pdf_files):
text = ""
for pdf in pdf_files:
try:
pdf_reader = PdfReader(pdf.name)
for page in pdf_reader.pages:
extracted = page.extract_text()
if extracted:
text += extracted + "\n"
except Exception as e:
return f"Error reading PDF: {str(e)}"
return text
def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
return text_splitter.split_text(text)
def create_vector_store(text_chunks):
try:
embeddings = GoogleGenerativeAIEmbeddings(model="text-embedding-004", google_api_key=GOOGLE_API_KEY)
vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
vector_store.save_local(INDEX_PATH)
return "PDFs processed successfully! Vector store saved. Now you can ask questions."
except Exception as e:
return f"Error creating vector store: {str(e)}"
def load_vector_store():
try:
embeddings = GoogleGenerativeAIEmbeddings(model="text-embedding-004", google_api_key=GOOGLE_API_KEY)
if os.path.exists(INDEX_PATH):
return FAISS.load_local(INDEX_PATH, embeddings, allow_dangerous_deserialization=True)
return None
except Exception as e:
return None
def get_conversational_chain():
prompt_template = """
Answer the question as detailed as possible from the provided context.
If the answer is not in the provided context, respond with "answer is not available in the context".
Do not provide incorrect information.
Context:
{context}
Question:
{question}
Answer:
"""
model = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.3, google_api_key=GOOGLE_API_KEY)
prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
return load_qa_chain(model, chain_type="stuff", prompt=prompt)
def query_pdf(user_question):
vector_store = load_vector_store()
if vector_store is None:
return "Please process a PDF first by uploading and submitting it."
try:
docs = vector_store.similarity_search(user_question, k=4)
chain = get_conversational_chain()
response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
return response["output_text"]
except Exception as e:
return f"Error querying the PDF: {str(e)}"
def process_pdfs(pdf_files):
if not pdf_files:
return "Please upload at least one PDF."
raw_text = get_pdf_text(pdf_files)
if "Error" in raw_text:
return raw_text
if not raw_text.strip():
return "No extractable text found in the uploaded PDFs."
text_chunks = get_text_chunks(raw_text)
result = create_vector_store(text_chunks)
return result
with gr.Blocks(title="Chat with PDF") as demo:
gr.Markdown("## Chat with PDF 💁")
pdf_input = gr.File(file_types=[".pdf"], label="Upload PDF(s)", file_count="multiple")
process_button = gr.Button("Submit & Process")
status_output = gr.Textbox(label="Status", placeholder="Status updates will appear here...")
question_input = gr.Textbox(label="Ask a Question from the PDF")
answer_output = gr.Textbox(label="Reply", placeholder="Answers will appear here...")
ask_button = gr.Button("Get Answer")
process_button.click(process_pdfs, inputs=[pdf_input], outputs=[status_output])
ask_button.click(query_pdf, inputs=[question_input], outputs=[answer_output])
if __name__ == "__main__":
demo.launch() |