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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 OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
openai_api_key = os.environ.get("OPENAI_API_KEY")
class AdvancedPdfChatbot:
def __init__(self, openai_api_key):
os.environ["OPENAI_API_KEY"] = openai_api_key
self.embeddings = OpenAIEmbeddings()
self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
self.llm = ChatOpenAI(temperature=0.1,model_name='gpt-4o-mini',max_tokens=3000, top_p = 0.1)
self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
self.qa_chain = None
self.pdf_path = None
self.template = """
I want you to act as a chat assistant for research more similar to ChatGPT itself, specifically focusing on knowledge retrieval. Provide detailed responses with great attention to context understanding and structured format. Be specific and detail-oriented in your responses, ensuring accuracy and depth in information provided.
Note: If you dont know the answer from the given knowledge base say you dont know it, **dont hallucinate**.
Context: {context}
Question: {question}
Answer:
"""
self.prompt = PromptTemplate(template=self.template, input_variables=["context", "question"])
def load_and_process_pdf(self, pdf_path):
loader = PyPDFLoader(pdf_path)
documents = loader.load()
texts = self.text_splitter.split_documents(documents)
self.db = FAISS.from_documents(texts, self.embeddings)
self.pdf_path = pdf_path
self.setup_conversation_chain()
def setup_conversation_chain(self):
self.qa_chain = ConversationalRetrievalChain.from_llm(
self.llm,
retriever=self.db.as_retriever(),
memory=self.memory,
combine_docs_chain_kwargs={"prompt": self.prompt}
)
def chat(self, query):
if not self.qa_chain:
return "Please upload a PDF first."
result = self.qa_chain({"question": query})
return result['answer']
def get_pdf_path(self):
# Return the stored PDF path
if self.pdf_path:
return self.pdf_path
else:
return "No PDF uploaded yet."
# Initialize the chatbot
pdf_chatbot = AdvancedPdfChatbot(openai_api_key)
def upload_pdf(pdf_file):
if pdf_file is None:
return "Please upload a PDF file."
file_path = pdf_file.name
pdf_chatbot.load_and_process_pdf(file_path)
return file_path
def respond(message, history):
bot_message = pdf_chatbot.chat(message)
history.append((message, bot_message))
return "", history
def clear_chatbot():
pdf_chatbot.memory.clear()
return []
def get_pdf_path():
# Call the method to return the current PDF path
return pdf_chatbot.get_pdf_path()
# Create the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# PDF Chatbot")
with gr.Row():
pdf_upload = gr.File(label="Upload PDF", file_types=[".pdf"])
upload_button = gr.Button("Process PDF")
upload_status = gr.Textbox(label="Upload Status")
upload_button.click(upload_pdf, inputs=[pdf_upload], outputs=[upload_status])
path_button = gr.Button("Get PDF Path")
pdf_path_display = gr.Textbox(label="Current PDF Path")
chatbot_interface = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
msg.submit(respond, inputs=[msg, chatbot_interface], outputs=[msg, chatbot_interface])
clear.click(clear_chatbot, outputs=[chatbot_interface])
path_button.click(get_pdf_path, outputs=[pdf_path_display])
if __name__ == "__main__":
demo.launch()
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