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