import requests import json import gradio as gr # from concurrent.futures import ThreadPoolExecutor import pdfplumber import pandas as pd import langchain import time from cnocr import CnOcr import pinecone import openai from langchain.vectorstores import Pinecone from langchain.embeddings.openai import OpenAIEmbeddings from langchain.text_splitter import CharacterTextSplitter # from langchain.document_loaders import PyPDFLoader from langchain.document_loaders import UnstructuredWordDocumentLoader from langchain.document_loaders import UnstructuredPowerPointLoader # from langchain.document_loaders.image import UnstructuredImageLoader from langchain.chains.question_answering import load_qa_chain from langchain import OpenAI from sentence_transformers import SentenceTransformer, models, util word_embedding_model = models.Transformer('sentence-transformers/all-MiniLM-L6-v2', do_lower_case=True) pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode='cls') embedder = SentenceTransformer(modules=[word_embedding_model, pooling_model]) ocr = CnOcr() # chat_url = 'https://Raghav001-API.hf.space/sale' chat_url = 'https://Raghav001-API.hf.space/chatpdf' chat_emd = 'https://Raghav001-API.hf.space/embedd' headers = { 'Content-Type': 'application/json', } # thread_pool_executor = ThreadPoolExecutor(max_workers=4) history_max_len = 500 all_max_len = 3000 # Initialize Pinecone client and create an index pinecone.init(api_key='d0a5b89b-b901-4b47-bc99-38b93695390d',environment = 'asia-southeast1-gcp') index = pinecone.Index(index_name='test') def get_emb(text): emb_url = 'https://Raghav001-API.hf.space/embeddings' data = {"content": text} try: result = requests.post(url=emb_url, data=json.dumps(data), headers=headers ) print("--------------------------------Embeddings--------------------------------------") print(result.json()['data'][0]['embedding']) return result.json()['data'][0]['embedding'] except Exception as e: print('data', data, 'result json', result.json()) def doc_emb(doc: str): texts = doc.split('\n') # futures = [] emb_list = embedder.encode(texts) print('emb_list',emb_list) # for text in texts: # futures.append(thread_pool_executor.submit(get_emb, text)) # for f in futures: # emb_list.append(f.result()) print('\n'.join(texts)) gr.Textbox.update(value="") return texts, emb_list, gr.Textbox.update(visible=True), gr.Button.update(visible=True), gr.Markdown.update( value="""success ! Let's talk"""), gr.Chatbot.update(visible=True) def get_response(msg, bot, doc_text_list, doc_embeddings): # future = thread_pool_executor.submit(get_emb, msg) gr.Textbox.update(value="") now_len = len(msg) req_json = {'question': msg} his_bg = -1 for i in range(len(bot) - 1, -1, -1): if now_len + len(bot[i][0]) + len(bot[i][1]) > history_max_len: break now_len += len(bot[i][0]) + len(bot[i][1]) his_bg = i req_json['history'] = [] if his_bg == -1 else bot[his_bg:] # query_embedding = future.result() query_embedding = embedder.encode([msg]) cos_scores = util.cos_sim(query_embedding, doc_embeddings)[0] score_index = [[score, index] for score, index in zip(cos_scores, [i for i in range(len(cos_scores))])] score_index.sort(key=lambda x: x[0], reverse=True) print('score_index:\n', score_index) print('doc_emb_state', doc_emb_state) index_set, sub_doc_list = set(), [] for s_i in score_index: doc = doc_text_list[s_i[1]] if now_len + len(doc) > all_max_len: break index_set.add(s_i[1]) now_len += len(doc) # Maybe the paragraph is truncated wrong, so add the upper and lower paragraphs if s_i[1] > 0 and s_i[1] -1 not in index_set: doc = doc_text_list[s_i[1]-1] if now_len + len(doc) > all_max_len: break index_set.add(s_i[1]-1) now_len += len(doc) if s_i[1] + 1 < len(doc_text_list) and s_i[1] + 1 not in index_set: doc = doc_text_list[s_i[1]+1] if now_len + len(doc) > all_max_len: break index_set.add(s_i[1]+1) now_len += len(doc) index_list = list(index_set) index_list.sort() for i in index_list: sub_doc_list.append(doc_text_list[i]) req_json['doc'] = '' if len(sub_doc_list) == 0 else '\n'.join(sub_doc_list) data = {"content": json.dumps(req_json)} print('data:\n', req_json) result = requests.post(url=chat_url, data=json.dumps(data), headers=headers ) res = result.json()['content'] bot.append([msg, res]) return bot[max(0, len(bot) - 3):] def up_file(fls): doc_text_list = [] names = [] print(names) for i in fls: names.append(str(i.name)) pdf = [] docs = [] pptx = [] for i in names: if i[-3:] == "pdf": pdf.append(i) elif i[-4:] == "docx": docs.append(i) else: pptx.append(i) #Pdf Extracting for idx, file in enumerate(pdf): print("11111") #print(file.name) with pdfplumber.open(file) as pdf: for i in range(len(pdf.pages)): # Read page i+1 of a PDF document page = pdf.pages[i] res_list = page.extract_text().split('\n')[:-1] for j in range(len(page.images)): # Get the binary stream of the image img = page.images[j] file_name = '{}-{}-{}.png'.format(str(time.time()), str(i), str(j)) with open(file_name, mode='wb') as f: f.write(img['stream'].get_data()) try: res = ocr.ocr(file_name) # res = PyPDFLoader(file_name) except Exception as e: res = [] if len(res) > 0: res_list.append(' '.join([re['text'] for re in res])) tables = page.extract_tables() for table in tables: # The first column is used as the header df = pd.DataFrame(table[1:], columns=table[0]) try: records = json.loads(df.to_json(orient="records", force_ascii=False)) for rec in records: res_list.append(json.dumps(rec, ensure_ascii=False)) except Exception as e: res_list.append(str(df)) doc_text_list += res_list doc_text_list = [str(text).strip() for text in doc_text_list if len(str(text).strip()) > 0] # print(doc_text_list) return gr.Textbox.update(value='\n'.join(doc_text_list), visible=True), gr.Button.update( visible=True), gr.Markdown.update( value="Processing") def get_answer(query_live): llm = OpenAI(temperature=0, openai='aaa') qa_chain = load_qa_chain(llm,chain_type='stuff') query = query_live docs = docstore.similarity_search(query) qa_chain.run(input_documents = docs, question = query) with gr.Blocks() as demo: with gr.Row(): with gr.Column(): file = gr.File(file_types=['.pdf'], label='Click to upload Document', file_count='multiple') doc_bu = gr.Button(value='Submit', visible=False) txt = gr.Textbox(label='result', visible=False) doc_text_state = gr.State([]) doc_emb_state = gr.State([]) with gr.Column(): md = gr.Markdown("Please Upload the PDF") chat_bot = gr.Chatbot(visible=False) msg_txt = gr.Textbox(visible = False) chat_bu = gr.Button(value='Clear', visible=False) file.change(up_file, [file], [txt, doc_bu, md]) #hiding the text doc_bu.click(doc_emb, [txt], [doc_text_state, doc_emb_state, msg_txt, chat_bu, md, chat_bot]) msg_txt.submit(get_response, [msg_txt, chat_bot,doc_text_state, doc_emb_state], [chat_bot],queue=False) chat_bu.click(lambda: None, None, chat_bot, queue=False) if __name__ == "__main__": demo.queue().launch(show_api=False) # demo.queue().launch(share=False, server_name='172.22.2.54', server_port=9191)