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import os |
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import gradio as gr |
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from huggingface_hub import Repository |
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from text_generation import Client |
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HF_TOKEN = os.environ.get("HF_TOKEN", None) |
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API_TOKEN = os.environ.get("API_TOKEN", None) |
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API_URL = os.environ.get("API_URL", None) |
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API_URL = "https://api-inference.huggingface.co/models/timdettmers/guanaco-33b-merged" |
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client = Client( |
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API_URL, |
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headers={"Authorization": f"Bearer {API_TOKEN}"}, |
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) |
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repo = None |
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def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep): |
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past = [] |
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for data in chatbot: |
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user_data, model_data = data |
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if not user_data.startswith(user_name): |
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user_data = user_name + user_data |
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if not model_data.startswith(sep + assistant_name): |
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model_data = sep + assistant_name + model_data |
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past.append(user_data + model_data.rstrip() + sep) |
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if not inputs.startswith(user_name): |
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inputs = user_name + inputs |
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total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip() |
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return total_inputs |
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def has_no_history(chatbot, history): |
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return not chatbot and not history |
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info = {"intro":"""My name is Karthik raja, I live in Chennai, India. I recently completed my bachelors at SSN College of Engineering.He is an experienced programmer, I have honed my skills in competitive programming and machine learning. Through my work in these areas, I have |
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developed a strong foundation in data analysis and model selection, which has allowed me to achieve high accuracy in my projects. My expertise |
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extends to computer vision and natural language processing, and I am particularly interested in exploring cutting‐edge techniques like few‐shot |
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learning and other meta‐learning methods to enhance NLP applications. I have taken part in several ML competitions, including Imageclef and |
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Hasoc, and have consistently ranked highly. I have also been exploring multilingual model analysis, leveraging the power of few‐shot learning |
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to develop highly efficient and accurate models. Overall, my expertise in programming, machine learning, and NLP, combined with my passion |
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for exploring cutting‐edge techniques such as few‐shot learning, make me a valuable asset to any team. """ |
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,"education": """I completed my 10th grade in P.S.Senior Secondary School in 2017 and was a school topper.I completed my 12th grade in P.S.Senior Secondary School in 2019 and passed with 91%. |
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I completed my bachelors in SSN College Of Engineering Chennai, India in Computer Science and Engineering with a consolidated CGPA score of 8.9, betweeen 2019 to 2023.And this is my highest degree of qualification. |
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I haven't done Masters or any Postgraduate degrees yet.""" |
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,"industry": """ |
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I did my industry internship at Citi Corp,India as a Website Developer between May 2022 and Aug 2022. |
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In this internship opportunity I was able to collabore with with a four‐member team to develop a full fledged website using springtools with data extraction from H2 database. |
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""" |
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,"research": """I have a stellar research profile as well, I have published 3 papers in conferences and 1 is underreview in a journal. |
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My first publication is on Neural Network for TB analysis which was created for CEURS-WS conference Image Clef contest published in 2021. |
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Second being Abusive and Threatening Language |
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Detection in Native Urdu Script Tweets Exploring Four Conventional Machine Learning Techniques and MLP |
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Fire conference where we used Naive Bayes,LSTM BERT with different tokenizing methods with translation. |
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Third being paper titled Offensive Text Prediction using Machine |
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Learning and Deep Learning Approaches Ceur‐ws conference, where we explored bagging like techniques with the models mentioned above. |
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I was able to publish my Final Year Project in a journal,Counterfactual Detection Neural Processing |
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Letters, this is under review. |
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Apart from papers I have also contributed to creation of application for the |
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National Institute of Siddha – Ministry of AYUSH(GoI), AIIMS Jodhpur, the Siddha Expert System between Sep‐Nov 2022, which was used to |
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Analyzed Siddha prognosis transcripts written in the Tamil regional language and Built an expert system to perform a nine‐way classification of Siddha diseases. |
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I was also able to work for the Tamil Nadu State Police for Suspicious Vehicle Tracking System through multiple cameras between Feb 2022 ‐ July 2022. |
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Here we Analysed various DeepLearning models for feature extraction, techniques like key frame extraction and Explored various matching models like siamese and metric mesures like cosine distance for vehicle Reid. |
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We had to Use prebuilt kalman filter and DeepSORT models to increase precision and to avoid occlusion.In this project we Experimented with various object detection, localization, and tracking models. |
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In another one of my research endevors we were able to develop an arm prototype for a underwater vehicle for UnderWater Remote Operated Vehicle Lab in my undergrad college. |
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For this I Helped design an grabber arm using CAD, trained Yolo models for object detection and worked on design and movement for the arm, |
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Some of my other projects include |
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Non‐residential Builtup Area classification from medium resolution satellite Chennai, India |
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India Meteorological Department (IMD), Ministry of Earth Sciences (MoES). (for this we won the Smart India |
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Hackathon ). |
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Person ReId in a large scale system in undergrad college. |
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""" |
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,"open-src":""" |
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I have also contributed to open source and have regularly been part of octoberFest, and have contributed to popular libraries like Ivy Unify, for more info check out https://github.com/kitrak-rev. |
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These are my profile links |
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Connect with me on either: https://www.linkedin.com/in/kitrak-rev/, or https://github.com/kitrak-rev. |
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""" |
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,"clubs":"""In my college I held the following positions: |
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• IEEECS Student Chapter Core Commitee Member (Vice Chair) |
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• IEEE Student Chapter Core Commitee Member (Treasurer) |
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• ACM Student Chapter Core Commitee Member (Event Deputy Head) |
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• Computer Society of India Student Chapter Core Committee Member (Vice Chair) |
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• SSN Coding Club Commitee Member (Competitive Programming Team) |
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I was given the task to explain BART model and its usage in Dall‐e like models in IVA pre‐conference workshop 2023.""" |
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,"description":""" |
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My fullname is karthik Raja Anandan.I am a male. I am 22 years old. I was born in 2001 in the month of February""" |
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} |
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total_info = " ".join([item for key,item in info.items()]) |
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header = total_info+""" |
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Assume you are karthik Raja Anandan mentioned previously. With this in mind, give polite answers to the following questions in first person. |
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You are karthik Raja from now on, speak accordingly and answer the following, without asking any questions about them """ |
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prompt_template = "### Human:" +header+" {query}\n### Assistant:{response}" |
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def generate( |
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user_message, |
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chatbot, |
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history, |
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temperature, |
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top_p, |
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max_new_tokens, |
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repetition_penalty, |
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): |
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if not user_message: |
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print("Empty input") |
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history.append(user_message) |
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past_messages = [] |
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for data in chatbot: |
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user_data, model_data = data |
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past_messages.extend( |
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[{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}] |
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) |
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if len(past_messages) < 1: |
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prompt = header + prompt_template.format(query=user_message, response="") |
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else: |
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prompt = header |
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for i in range(0, len(past_messages), 2): |
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intermediate_prompt = prompt_template.format(query=past_messages[i]["content"], response=past_messages[i+1]["content"]) |
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print("intermediate: ", intermediate_prompt) |
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prompt = prompt + '\n' + intermediate_prompt |
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prompt = prompt + prompt_template.format(query=user_message, response="") |
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generate_kwargs = { |
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"temperature": temperature, |
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"top_p": top_p, |
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"max_new_tokens": max_new_tokens, |
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} |
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temperature = float(temperature) |
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if temperature < 1e-2: |
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temperature = 1e-2 |
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top_p = float(top_p) |
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generate_kwargs = dict( |
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temperature=temperature, |
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max_new_tokens=max_new_tokens, |
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top_p=top_p, |
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repetition_penalty=repetition_penalty, |
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do_sample=True, |
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truncate=999, |
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seed=42, |
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) |
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stream = client.generate_stream( |
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prompt, |
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**generate_kwargs, |
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) |
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output = "" |
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for idx, response in enumerate(stream): |
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if response.token.text == '': |
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break |
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if response.token.special: |
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continue |
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output += response.token.text |
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if idx == 0: |
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history.append(" " + output) |
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else: |
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history[-1] = output |
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chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)] |
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yield chat, history, user_message, "" |
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return chat, history, user_message, "" |
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examples = [ |
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"Give a short summary about you" |
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] |
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def clear_chat(): |
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return [], [] |
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def process_example(args): |
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for [x, y] in generate(args): |
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pass |
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return [x, y] |
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title = """<h1 align="center">Karthik Raja AI Clone 🙋♂️ </h1>""" |
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custom_css = """ |
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#banner-image { |
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display: block; |
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margin-left: auto; |
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margin-right: auto; |
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} |
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#chat-message { |
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font-size: 14px; |
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min-height: 300px; |
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} |
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""" |
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with gr.Blocks(analytics_enabled=False, css=custom_css) as demo: |
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gr.HTML(title) |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown( |
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""" |
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💻 This demo attempts to be a ai-clone of a person with prompts on the Guanaco 33B model, released together with the paper [QLoRA](https://arxiv.org/abs/2305.14314) |
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<br /> |
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Note: The information given by the AI-clone may not be 100% accurate, check with the bot's owner to confirm. |
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""" |
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) |
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with gr.Row(): |
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with gr.Box(): |
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output = gr.Markdown("Ask any questions that you want to ask Karthik Raja") |
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chatbot = gr.Chatbot(elem_id="chat-message", label="AI-clone of Karthik Raja") |
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with gr.Row(): |
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with gr.Column(scale=3): |
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user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input") |
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with gr.Row(): |
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send_button = gr.Button("Send", elem_id="send-btn", visible=True) |
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clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True) |
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with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"): |
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temperature = gr.Slider( |
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label="Temperature", |
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value=0.7, |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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interactive=True, |
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info="Higher values produce more diverse outputs", |
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) |
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top_p = gr.Slider( |
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label="Top-p (nucleus sampling)", |
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value=0.9, |
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minimum=0.0, |
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maximum=1, |
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step=0.05, |
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interactive=True, |
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info="Higher values sample more low-probability tokens", |
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) |
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max_new_tokens = gr.Slider( |
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label="Max new tokens", |
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value=1024, |
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minimum=0, |
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maximum=2048, |
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step=4, |
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interactive=True, |
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info="The maximum numbers of new tokens", |
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) |
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repetition_penalty = gr.Slider( |
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label="Repetition Penalty", |
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value=1.2, |
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minimum=0.0, |
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maximum=10, |
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step=0.1, |
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interactive=True, |
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info="The parameter for repetition penalty. 1.0 means no penalty.", |
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) |
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with gr.Row(): |
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gr.Examples( |
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examples=examples, |
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inputs=[user_message], |
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cache_examples=False, |
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fn=process_example, |
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outputs=[output], |
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) |
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with gr.Row(): |
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gr.Markdown( |
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"Disclaimer: The model can produce factually incorrect output, and should not be relied on to produce " |
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"factually accurate information. The model was trained on various public datasets; while great efforts " |
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"have been taken to clean the pretraining data, it is possible that this model could generate lewd, " |
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"biased, or otherwise offensive outputs.", |
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elem_classes=["disclaimer"], |
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) |
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history = gr.State([]) |
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last_user_message = gr.State("") |
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user_message.submit( |
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generate, |
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inputs=[ |
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user_message, |
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chatbot, |
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history, |
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temperature, |
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top_p, |
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max_new_tokens, |
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repetition_penalty, |
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], |
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outputs=[chatbot, history, last_user_message, user_message], |
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) |
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send_button.click( |
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generate, |
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inputs=[ |
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user_message, |
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chatbot, |
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history, |
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temperature, |
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top_p, |
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max_new_tokens, |
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repetition_penalty, |
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], |
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outputs=[chatbot, history, last_user_message, user_message], |
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) |
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clear_chat_button.click(clear_chat, outputs=[chatbot, history]) |
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demo.queue(concurrency_count=16).launch() |
