initial commit

app.py

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+import os
+
+import gradio as gr
+from huggingface_hub import Repository
+from text_generation import Client
+
+# from dialogues import DialogueTemplate
+from share_btn import (community_icon_html, loading_icon_html, share_btn_css,
+                       share_js)
+
+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
+
+
+
+header = """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
+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.
+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.
+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.
+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.
+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.
+Connect with me on either: https://www.linkedin.com/in/kitrak-rev/, or https://github.com/kitrak-rev.
+These are my profile links
+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.
+My fullname is karthik Raja Anandan.
+Assume you are karthik Raja Anandan mentioned in the above text, keeping this in mind, give polite answers to the following questions in first person. """
+prompt_template = "###"+header+" Human: {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 = [
+    "A Llama entered in my garden, what should I do?"
+]
+
+
+def clear_chat():
+    return [], []
+
+
+def process_example(args):
+    for [x, y] in generate(args):
+        pass
+    return [x, y]
+
+
+title = """<h1 align="center">Guanaco Playground 💬</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(debug=True)

requirements.txt

@@ -0,0 +1,8 @@
+einops
+gradio
+torch
+transformers
+sentencepiece
+bitsandbytes
+accelerate
+text-generation