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from huggingface_hub import InferenceClient |
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import gradio as gr |
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client = InferenceClient( |
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"mistralai/Mixtral-8x7B-Instruct-v0.1" |
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) |
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communities = ''' |
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<h3 align="left">Communities:</h3> |
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<ul> |
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<li> |
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<p><strong><em><a href="https://developers.google.com/community/experts/directory?text=rishiraj">Google Developer Expert</a> in Machine Learning (Generative AI)</em></strong><br /> |
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A Google Developers Expert (GDE) is a person recognized by Google as having exemplary expertise in web technologies or Google Developers products.</p> |
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</li> |
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<li> |
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<p><strong><em><a href="https://twitter.com/TFUGKol">TensorFlow User Group Kolkata</a> (Organizer)</em></strong><br /> |
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TensorFlow User Groups (TFUGs) are communities of developers, engineers, data scientists, and ML practitioners who are passionate about TensorFlow and related technologies.</p> |
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</li> |
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<li> |
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<p><strong><em><a href="https://gdg.community.dev/gdg-cloud-kolkata/">Google Developer Groups Cloud Kolkata</a> (Volunteer)</em></strong><br /> |
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Google Developer Groups (GDGs) Cloud are communities of developers, engineers, and cloud architects who are passionate about Google Cloud Platform and related technologies.</p> |
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</li> |
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</ul>''' |
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def show_info(section): |
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if section == "Experiences": |
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return "Details about Rishiraj's experiences..." |
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elif section == "Communities": |
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return communities |
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elif section == "Recommendations": |
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return "Rishiraj's recommendations..." |
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elif section == "Conferences": |
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return "Conferences attended by Rishiraj..." |
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else: |
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return "Select a section to display information." |
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with gr.Blocks() as app: |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("# Hi 👋, I'm Rishiraj Acharya (ঋষিরাজ আচার্য্য)") |
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gr.Markdown("## Google Developer Expert in ML ✨ | Hugging Face Fellow 🤗 | GSoC '22 at TensorFlow 👨🏻🔬 | TFUG Kolkata Organizer 🎙️ | Kaggle Master 🧠 | Dynopii ML Engineer 👨🏻💻") |
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gr.Markdown("**I work with natural language understanding, machine translation, named entity recognition, question answering, topic segmentation, and automatic speech recognition. My work typically relies on very large quantities of data and innovative methods in deep learning to tackle user challenges around the world — in languages from around the world. My areas of work include Natural Language Engineering, Language Modeling, Text-to-Speech Software Engineering, Speech Frameworks Engineering, Data Science, and Research.**") |
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gr.Markdown("⚡ Fun fact **I’m a national level Chess player, a swimming champion and I can lecture for hours on the outer reaches of space and the craziness of astrophysics.**") |
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gr.HTML(value='<br><p align="center"><a href="https://twitter.com/rishirajacharya" target="blank"><img align="center" src="https://raw.githubusercontent.com/rahuldkjain/github-profile-readme-generator/master/src/images/icons/Social/twitter.svg" alt="rishirajacharya" height="30" width="40" /></a></p>') |
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with gr.Column(): |
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gr.Image("profile.png") |
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with gr.Row(): |
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section_dropdown = gr.Dropdown(["Experiences", "Communities", "Recommendations", "Conferences"], label="Select Information to Display") |
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info_display = gr.HTML(label="Information") |
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section_dropdown.change(show_info, inputs=section_dropdown, outputs=info_display) |
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def format_prompt(message, history): |
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prompt = "<s>" |
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for user_prompt, bot_response in history: |
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prompt += f"[INST] {user_prompt} [/INST]" |
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prompt += f" {bot_response}</s> " |
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prompt += f"[INST] {message} [/INST]" |
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return prompt |
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def generate( |
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prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, |
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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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seed=42, |
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) |
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formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history) |
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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output += response.token.text |
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yield output |
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return output |
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additional_inputs=[ |
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gr.Textbox( |
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label="System Prompt", |
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max_lines=1, |
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interactive=True, |
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), |
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gr.Slider( |
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label="Temperature", |
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value=0.9, |
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minimum=0.0, |
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maximum=1.0, |
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step=0.05, |
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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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gr.Slider( |
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label="Max new tokens", |
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value=256, |
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minimum=0, |
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maximum=1048, |
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step=64, |
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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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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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value=0.90, |
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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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gr.Slider( |
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label="Repetition penalty", |
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value=1.2, |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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interactive=True, |
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info="Penalize repeated tokens", |
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) |
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] |
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examples=[["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,], |
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["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,], |
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] |
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llm = gr.ChatInterface( |
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fn=generate, |
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chatbot=gr.Chatbot(show_label=True, show_share_button=True, show_copy_button=True, likeable=True, layout="bubble"), |
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additional_inputs=additional_inputs, |
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title="Hi 👋, I'm Rishiraj Acharya (ঋষিরাজ আচার্য্য)", |
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examples=examples, |
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concurrency_limit=20, |
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) |
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demo = gr.TabbedInterface([app, llm], ["About", "Chat"]) |
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demo.launch() |
