Spaces:
Sleeping
Sleeping
| #Import libraries | |
| import numpy as np | |
| import pandas as pd | |
| import transformers | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| import torch | |
| import re | |
| import gradio as gr | |
| #Load model | |
| device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" | |
| model = torch.load('finance_chatbot_gpt2_complete_model.pt', map_location=torch.device('cpu')) | |
| model = model.to(device) | |
| #Get LLM tokenizer | |
| tokenizer = GPT2Tokenizer.from_pretrained("gpt2") | |
| tokenizer.add_special_tokens({"pad_token": "<pad>", | |
| "bos_token": "<startofstring>", | |
| "eos_token": "<endofstring>"}) | |
| tokenizer.add_tokens(["<bot>:"]) | |
| #Inference function | |
| def infer_1(inp, chat_history): | |
| inp_1 = "<startofstring>"+inp+"<bot>:" | |
| inp_tok = tokenizer(inp_1, return_tensors="pt") | |
| X = inp_tok["input_ids"].to(device) | |
| a = inp_tok["attention_mask"].to(device) | |
| output = model.generate(X, attention_mask=a) | |
| output = tokenizer.decode(output[0]) | |
| #Remove the user input part seq | |
| output = output[len(inp_1):] | |
| #Remove next line char | |
| output = re.sub(r'\n', '', output) | |
| #Remove <endofstring>|<pad> tokens from the end | |
| output = re.sub(r'<endofstring>|<pad>', '', output) | |
| #Append to chat history | |
| chat_history.append((inp, output)) | |
| return '', chat_history | |
| #Ancillary variables | |
| project_heading = '<html><head><meta content="text/html; charset=UTF-8" http-equiv="content-type"><style type="text/css">ol{margin:0;padding:0}table td,table th{padding:0}.c1{-webkit-text-decoration-skip:none;color:#000000;font-weight:700;text-decoration:underline;vertical-align:baseline;text-decoration-skip-ink:none;font-size:26pt;font-family:"Arial";font-style:normal}.c2{padding-top:0pt;padding-bottom:3pt;line-height:1.15;page-break-after:avoid;orphans:2;widows:2;text-align:center}.c0{background-color:#ffffff;max-width:468pt;padding:72pt 72pt 72pt 72pt}.title{padding-top:0pt;color:#000000;font-size:26pt;padding-bottom:3pt;font-family:"Arial";line-height:1.15;page-break-after:avoid;orphans:2;widows:2;text-align:left}.subtitle{padding-top:0pt;color:#666666;font-size:15pt;padding-bottom:16pt;font-family:"Arial";line-height:1.15;page-break-after:avoid;orphans:2;widows:2;text-align:left}li{color:#000000;font-size:11pt;font-family:"Arial"}p{margin:0;color:#000000;font-size:11pt;font-family:"Arial"}h1{padding-top:20pt;color:#000000;font-size:20pt;padding-bottom:6pt;font-family:"Arial";line-height:1.15;page-break-after:avoid;orphans:2;widows:2;text-align:left}h2{padding-top:18pt;color:#000000;font-size:16pt;padding-bottom:6pt;font-family:"Arial";line-height:1.15;page-break-after:avoid;orphans:2;widows:2;text-align:left}h3{padding-top:16pt;color:#434343;font-size:14pt;padding-bottom:4pt;font-family:"Arial";line-height:1.15;page-break-after:avoid;orphans:2;widows:2;text-align:left}h4{padding-top:14pt;color:#666666;font-size:12pt;padding-bottom:4pt;font-family:"Arial";line-height:1.15;page-break-after:avoid;orphans:2;widows:2;text-align:left}h5{padding-top:12pt;color:#666666;font-size:11pt;padding-bottom:4pt;font-family:"Arial";line-height:1.15;page-break-after:avoid;orphans:2;widows:2;text-align:left}h6{padding-top:12pt;color:#666666;font-size:11pt;padding-bottom:4pt;font-family:"Arial";line-height:1.15;page-break-after:avoid;font-style:italic;orphans:2;widows:2;text-align:left}</style></head><body class="c0 doc-content"><p class="c2 title" id="h.gqbolkoo61b7"><span class="c1">Personal Finance ChatBot</span></p></body></html>' | |
| project_des = """## Project Description | |
| This Chatbot is based on a fine-tuned version of 'GPT2-Small'. Primarily, the text from Robert Kiyosaki's book, "Rich Dad Poor Dad," was processed and used in training. | |
| **Note:** This project is built for educational purposes, and it is important to note that the responses generated by the model should not be interpreted literally. | |
| """ | |
| sample_questions = ["Where should I invest in to protect my assets?", "What is the result of people working all their lives for someone else?", "Why do you want to earn more passive income?", | |
| "What inspires winners?", "What do most people fail to realize in life?", "Why do people struggle financially?", "What is the name of your developer?", | |
| "What do poor people do with their eggs?"] | |
| data_processing_story = """ | |
| <h2>Data Collection and Processing</h2> | |
| <h3>Step 1: Extracted raw text data from two broad sources</h3> | |
| <p>1.1. Digital copy of 'Rich Dad Poor Dad' by Parsing the book, Link: <a href="https://drive.google.com/file/d/1vTrfwcqI5rMVq7CsVEy38aRwPTjN1s6T/view?usp=sharing">Rich Dad Poor Dad Book</a></p> | |
| <p>1.2. Scraping websites with summary or quotes from the book such as Link: <a href="https://www.goodreads.com/work/quotes/3366043-rich-dad-poor-dad?page=1">Goodreads Quotes</a></p> | |
| <h3>Step 2: Data Cleaning and Preprocessing</h3> | |
| <p>2.1. Removed irregularities from the text data, e.g., additional comments, quotes, special characters, spaces, indentations, etc.</p> | |
| <p>2.2. Created batches of text with 512 words each, ascertaining appropriate text boundaries for the next stage of the pipeline</p> | |
| <h3>Step 3: Extracted data in the form of question-answer to create a conversational dataset using open source models</h3> | |
| <p>3.1. Used FAQ module from QUESTgen.AI's API, which produced question and one-word answer pairs from the given text, Repo Link: <a href="https://github.com/ramsrigouthamg/Questgen.ai">QUESTgen.AI Repository</a></p> | |
| <p>3.2. Resulting answers didn't carry the complete context and were incomplete, thus arising the need for better and short answers</p> | |
| <p>3.3. Used the model 'roberta-base-squad2' by Deepset for extracting meaningful answers, Model Card Link: <a href="https://huggingface.co/deepset/roberta-base-squad2">roberta-base-squad2 Model Card</a></p> | |
| <h2>Processed Dataset:</h2> | |
| """ | |
| model_details_HTML = """<h2>Model Details</h2> | |
| <p><strong>Base Model Name:</strong> GPT-2 (Small)</p> | |
| <p><strong>Training Process:</strong> Fine-tuned using GPT-2 with 23 epochs, 64 batch size, and 100 input tokens</p> | |
| <p><strong>Source Code:</strong> <a href="https://huggingface.co/spaces/yugamj/AI_text_detector_01/tree/main">GitHub Repository</a></p> | |
| <p><strong>Base Model Card:</strong> <a href="https://huggingface.co/gpt2">GPT-2 Model Card</a></p> | |
| <p><strong>Inference Time:</strong> Average response time is approximately 13 seconds</p> | |
| """ | |
| training_data = pd.read_csv('ques_ans_v5.csv', index_col = 0) | |
| #Launch with gradio | |
| with gr.Blocks() as demo: | |
| gr.HTML(project_heading) | |
| gr.Markdown(value = project_des) | |
| chatbot = gr.Chatbot(label = "Trained on scripts from \"Rich Dad Poor Dad\"") #value = [['Hey there User', 'Hey there CB']] | |
| msg = gr.Textbox(label = 'Press enter to submit!') | |
| clear = gr.ClearButton([msg, chatbot]) | |
| gr.Examples(sample_questions, msg) | |
| msg.submit(infer_1, [msg, chatbot], [msg, chatbot]) | |
| gr.HTML(data_processing_story) | |
| gr.DataFrame(training_data[['Question', 'answer_cb']].rename(columns = {'Question':'Question', 'answer_cb':'Answer'}), wrap = True) | |
| gr.HTML(model_details_HTML) | |
| demo.launch(share=True, debug=True) | |