import gradio as gr import os import time 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 import PromptTemplate from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor import requests from PIL import Image import torch # _template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. # Chat History: # {chat_history} # Follow Up Input: {question} # Standalone question:""" # CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template) # template = """ # You are given the following extracted parts of a long document and a question. Provide a short structured answer. # If you don't know the answer, look on the web. Don't try to make up an answer. # Question: {question} # ========= # {context} # ========= # Answer in Markdown:""" torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/val/png/20294671002019.png', 'chart_example.png') torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/multi_col_1081.png', 'chart_example_2.png') torch.hub.download_url_to_file('https://raw.githubusercontent.com/vis-nlp/ChartQA/main/ChartQA%20Dataset/test/png/18143564004789.png', 'chart_example_3.png') torch.hub.download_url_to_file('https://sharkcoder.com/files/article/matplotlib-bar-plot.png', 'chart_example_4.png') model_name = "google/matcha-chartqa" model = Pix2StructForConditionalGeneration.from_pretrained(model_name) processor = Pix2StructProcessor.from_pretrained(model_name) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) def filter_output(output): return output.replace("<0x0A>", "") def chart_qa(image, question): inputs = processor(images=image, text=question, return_tensors="pt").to(device) predictions = model.generate(**inputs, max_new_tokens=512) return filter_output(processor.decode(predictions[0], skip_special_tokens=True)) def loading_pdf(): return "Loading..." def pdf_changes(pdf_doc, open_ai_key): if open_ai_key is not None: os.environ['OPENAI_API_KEY'] = open_ai_key loader = OnlinePDFLoader(pdf_doc.name) documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) embeddings = OpenAIEmbeddings() db = Chroma.from_documents(texts, embeddings) retriever = db.as_retriever() global qa qa = ConversationalRetrievalChain.from_llm( llm=OpenAI(temperature=0.5), retriever=retriever, return_source_documents=True) return "Ready" else: return "You forgot OpenAI API key" def add_text(history, text): history = history + [(text, None)] return history, "" def bot(history): response = infer(history[-1][0], history) history[-1][1] = "" for character in response: history[-1][1] += character time.sleep(0.05) yield history def infer(question, history): res = [] for human, ai in history[:-1]: pair = (human, ai) res.append(pair) chat_history = res #print(chat_history) query = question result = qa({"question": query, "chat_history": chat_history}) #print(result) return result["answer"] css=""" #col-container {max-width: 700px; margin-left: auto; margin-right: auto;} """ title = """

YnP LangChain Test

Please specify OpenAI Key before use

""" # with gr.Blocks(css=css) as demo: # with gr.Column(elem_id="col-container"): # gr.HTML(title) # with gr.Column(): # openai_key = gr.Textbox(label="You OpenAI API key", type="password") # 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("Load pdf to langchain") # chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350) # 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, openai_key], 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() """functions""" def load_file(): return "Loading..." def load_xlsx(name): import pandas as pd xls_file = rf'{name}' data = pd.read_excel(xls_file) return data def table_loader(table_file, open_ai_key): import os from langchain.llms import OpenAI from langchain.agents import create_pandas_dataframe_agent from pandas import read_csv global agent if open_ai_key is not None: os.environ['OPENAI_API_KEY'] = open_ai_key else: return "Enter API" if table_file.name.endswith('.xlsx') or table_file.name.endswith('.xls'): data = load_xlsx(table_file.name) agent = create_pandas_dataframe_agent(OpenAI(temperature=0), data) return "Ready!" elif table_file.name.endswith('.csv'): data = read_csv(table_file.name) agent = create_pandas_dataframe_agent(OpenAI(temperature=0), data) return "Ready!" else: return "Wrong file format! Upload excel file or csv!" def run(query): from langchain.callbacks import get_openai_callback with get_openai_callback() as cb: response = (agent.run(query)) costs = (f"Total Cost (USD): ${cb.total_cost}") output = f'{response} \n {costs}' return output def respond(message, chat_history): import time bot_message = run(message) chat_history.append((message, bot_message)) time.sleep(0.5) return "", chat_history with gr.Blocks() as demo: with gr.Column(elem_id="col-container"): gr.HTML(title) key = gr.Textbox( show_label=False, placeholder="Your OpenAI key", type = 'password', ).style(container=False) # PDF processing tab with gr.Tab("PDFs"): with gr.Row(): with gr.Column(scale=0.5): langchain_status = gr.Textbox(label="Status", placeholder="", interactive=False) load_pdf = gr.Button("Load pdf to langchain") with gr.Column(scale=0.5): pdf_doc = gr.File(label="Load a pdf", file_types=['.pdf'], type="file") with gr.Row(): with gr.Column(scale=1): chatbot = gr.Chatbot([], elem_id="chatbot").style(height=350) with gr.Row(): with gr.Column(scale=0.85): question = gr.Textbox( show_label=False, placeholder="Enter text and press enter, or upload an image", ).style(container=False) with gr.Column(scale=0.15, min_width=0): clr_btn = gr.Button("Clear!") load_pdf.click(loading_pdf, None, langchain_status, queue=False) load_pdf.click(pdf_changes, inputs=[pdf_doc, key], outputs=[langchain_status], queue=True) question.submit(add_text, [chatbot, question], [chatbot, question]).then( bot, chatbot, chatbot ) # XLSX and CSV processing tab with gr.Tab("Spreadsheets"): with gr.Row(): with gr.Column(scale=0.5): status_sh = gr.Textbox(label="Status", placeholder="", interactive=False) load_table = gr.Button("Load csv|xlsx to langchain") with gr.Column(scale=0.5): raw_table = gr.File(label="Load a table file (xls or csv)", file_types=['.csv, xlsx, xls'], type="file") with gr.Row(): with gr.Column(scale=1): chatbot_sh = gr.Chatbot([], elem_id="chatbot").style(height=350) with gr.Row(): with gr.Column(scale=0.85): question_sh = gr.Textbox( show_label=False, placeholder="Enter text and press enter, or upload an image", ).style(container=False) with gr.Column(scale=0.15, min_width=0): clr_btn = gr.Button("Clear!") load_table.click(load_file, None, status_sh, queue=False) load_table.click(table_loader, inputs=[raw_table, key], outputs=[status_sh], queue=False) question_sh.submit(respond, [question_sh, chatbot_sh], [question_sh, chatbot_sh]) clr_btn.click(lambda: None, None, chatbot_sh, queue=False) with gr.Tab("Charts"): image = gr.Image(type="pil", label="Chart") question = gr.Textbox(label="Question") load_chart = gr.Button("Load chart and question!") answer = gr.Textbox(label="Model Output") load_chart.click(chart_qa, [image, question], answer) demo.queue(concurrency_count=3) demo.launch()