import gradio as gr import os import time import threading from langchain.document_loaders import OnlinePDFLoader from langchain.text_splitter import CharacterTextSplitter from langchain.llms import OpenAI from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.chains import ConversationalRetrievalChain from langchain.chat_models import ChatOpenAI from langchain.document_loaders import WebBaseLoader from langchain.chains.summarize import load_summarize_chain from langchain.chains.llm import LLMChain from langchain.prompts import PromptTemplate from langchain.chains.combine_documents.stuff import StuffDocumentsChain os.environ['OPENAI_API_KEY'] = os.getenv("Your_API_Key") # Global variable for tracking last interaction time last_interaction_time = 0 def loading_pdf(): return "Working on the upload. Also, pondering the usefulness of sporks..." # Inside Chroma mod def summary(self): num_documents = len(self.documents) avg_doc_length = sum(len(doc) for doc in self.documents) / num_documents return f"Number of documents: {num_documents}, Average document length: {avg_doc_length}" # PDF summary and query using stuffing def pdf_changes(pdf_doc): try: # Initialize loader and load documents loader = OnlinePDFLoader(pdf_doc.name) documents = loader.load() # Define the prompt for summarization prompt_template = """Write a concise summary of the following: "{text}" CONCISE SUMMARY:""" prompt = PromptTemplate.from_template(prompt_template) # Define the LLM chain with the specified prompt llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo-16k") llm_chain = LLMChain(llm=llm, prompt=prompt) # Initialize StuffDocumentsChain stuff_chain = StuffDocumentsChain( llm_chain=llm_chain, document_variable_name="text" ) # Generate summary using StuffDocumentsChain global full_summary full_summary = stuff_chain.run(documents) # Other existing logic for Chroma, embeddings, and retrieval embeddings = OpenAIEmbeddings() global db db = Chroma.from_documents(documents, embeddings) retriever = db.as_retriever() global qa qa = ConversationalRetrievalChain.from_llm( llm=OpenAI(temperature=0.2, model_name="gpt-3.5-turbo-16k", max_tokens=-1, n=2), retriever=retriever, return_source_documents=False ) return f"Ready. Full Summary loaded." except Exception as e: return f"Error processing PDF: {str(e)}" def clear_data(): global qa, db qa = None db = None return "Data cleared" def add_text(history, text): global last_interaction_time last_interaction_time = time.time() history = history + [(text, None)] return history, "" def bot(history): global full_summary if 'summary' in history[-1][0].lower(): # Check if the last question asks for a summary response = full_summary return full_summary else: response = infer(history[-1][0], history) sentences = ' \n'.join(response.split('. ')) formatted_response = f"**Bot:**\n\n{sentences}" history[-1][1] = formatted_response return history def infer(question, history): try: res = [] for human, ai in history[:-1]: pair = (human, ai) res.append(pair) chat_history = res query = question result = qa({"question": query, "chat_history": chat_history, "system": "This is a world-class summarizing AI, be helpful."}) return result["answer"] except Exception as e: return f"Error querying chatbot: {str(e)}" def auto_clear_data(): global qa, da, last_interaction_time if time.time() - last_interaction_time > 1000: qa = None db = None def periodic_clear(): while True: auto_clear_data() time.sleep(1000) threading.Thread(target=periodic_clear).start() css = """ #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} """ title = """

CauseWriter Chat with PDF • OpenAI

Upload a .PDF from your computer, click the "Load PDF to LangChain" button,
when everything is ready, you can start asking questions about the pdf. Limit ~11k words.
This version is set to erase chat history automatically after page timeout and uses OpenAI.

""" # Global variable for tracking last interaction time last_interaction_time = 0 full_summary = "" # Added global full_summary def update_summary_box(): global full_summary return {"summary_box": full_summary} with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.HTML(title) with gr.Column(): pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file") with gr.Row(): langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False) load_pdf = gr.Button("Convert PDF to Magic AI language") clear_btn = gr.Button("Clear Data") # New Textbox to display summary summary_box = gr.Textbox(label="Document Summary", placeholder="Summary will appear here.", interactive=False, rows=5, elem_id="summary_box") chatbot = gr.Chatbot([], elem_id="chatbot").style(height=450) question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter") submit_btn = gr.Button("Send Message") load_pdf.click(loading_pdf, None, langchain_status, queue=False) load_pdf.click(pdf_changes, inputs=[pdf_doc], outputs=[langchain_status], queue=False).then( update_summary_box, state={"summary_box": summary_box} ) # Then update the summary_box clear_btn.click(clear_data, outputs=[langchain_status], queue=False) question.submit(add_text, [chatbot, question], [chatbot, question]).then( bot, chatbot, chatbot ) submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then( bot, chatbot, chatbot ) demo.launch()