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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,model_name='gpt-4o-mini') | |
| self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True) | |
| self.qa_chain = None | |
| self.pdf_path = None | |
| self.template = """ | |
| You are a study partner assistant, students give you pdfs | |
| and you help them to answer their questions. | |
| Answer the question based on the most recent provided resources only. | |
| Give the most relevant answer. | |
| Context: {context} | |
| Question: {question} | |
| Answer: | |
| (Note :YOUR OUTPUT IS RENDERED IN PROPER PARAGRAPHS, BULLET POINTS, HEADLINES AND SUBTEXTS IN CLEAN PROFESSIONAL MANNER - this is very important) | |
| """ | |
| 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() | |