Update with latest

app.py

@@ -1,329 +1,26 @@
-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,
-    }
+message_list = []
+response_list = []
+from transformers import pipeline
 
-    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"
+messages = [
+    {"role": "system", "content": " You are Karthik a software engineer"},
+    {"role": "user", "content": "You are to impersonate as Karthik"},
+    {"role": "assistant", "content": "I am  Karthik a software engineer"},
+     {"role": "user", "content": "Who are you? "},
 ]
+chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3",max_new_tokens=5000)
 
+def mini_chatbot(message, history):
+    conversation = Conversation(text=message,
+                                past_user_inputs=message_list,
+                                generated_responses=response_list)
+    conversation = chatbot(conversation)
 
-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])
+    return conversation.generated_responses[-1]
 
-demo.queue(concurrency_count=16).launch()
+demo_chatbot = gr.ChatInterface(mini_chatbot,
+                                title="My Chatbot",
+                                description="Enter text to start chatting.")
+demo_chatbot.launch()