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import gradio as gr |
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import requests |
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import json |
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import os |
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API_KEY = os.getenv('API_KEY') |
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INVOKE_URL = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/5bde8f6f-7e83-4413-a0f2-7b97be33988e" |
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FETCH_URL_FORMAT = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/" |
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headers = { |
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"Authorization": f"Bearer {API_KEY}", |
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"Accept": "application/json", |
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"Content-Type": "application/json", |
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} |
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BASE_SYSTEM_MESSAGE = "I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning." |
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def clear_chat(chat_history_state, chat_message): |
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print("Clearing chat...") |
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chat_history_state = [] |
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chat_message = '' |
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return chat_history_state, chat_message |
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def user(message, history, system_message=None): |
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print(f"User message: {message}") |
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history = history or [] |
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if system_message: |
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history.append({"role": "system", "content": system_message}) |
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history.append({"role": "user", "content": message}) |
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return history |
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def call_nvidia_api(history, max_tokens, temperature, top_p): |
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payload = { |
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"messages": history, |
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"temperature": temperature, |
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"top_p": top_p, |
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"max_tokens": max_tokens, |
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"stream": False |
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} |
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print(f"Payload enviado: {payload}") |
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session = requests.Session() |
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response = session.post(INVOKE_URL, headers=headers, json=payload) |
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while response.status_code == 202: |
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request_id = response.headers.get("NVCF-REQID") |
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fetch_url = FETCH_URL_FORMAT + request_id |
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response = session.get(fetch_url, headers=headers) |
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response.raise_for_status() |
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response_body = response.json() |
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print(f"Payload recebido: {response_body}") |
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if response_body["choices"]: |
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assistant_message = response_body["choices"][0]["message"]["content"] |
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history.append({"role": "assistant", "content": assistant_message}) |
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return history |
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def chat(history, system_message, max_tokens, temperature, top_p): |
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print("Starting chat...") |
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updated_history = call_nvidia_api(history, max_tokens, temperature, top_p) |
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return updated_history, "" |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("Gemma 2B Free Demo") |
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description=""" |
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<div style="text-align: center; font-size: 1.5em; margin-bottom: 20px;"> |
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<strong>Explore the Capabilities of Gemma 2B</strong> |
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</div> |
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<p>Gemma is a family of lightweight, state-of-the art LLM open models from Google. |
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</p> |
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<p> <strong>How to Use:</strong></p> |
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<ol> |
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<li>Enter your <strong>message</strong> in the textbox to start a conversation or ask a question.</li> |
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<li>Adjust the <strong>Temperature</strong> and <strong>Top P</strong> sliders to control the creativity and diversity of the responses.</li> |
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<li>Set the <strong>Max Tokens</strong> slider to determine the length of the response.</li> |
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<li>Use the <strong>System Message</strong> textbox if you wish to provide a specific context or instruction for the AI.</li> |
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<li>Click <strong>Send message</strong> to submit your query and receive a response from Gemma 2B.</li> |
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<li>Press <strong>New topic</strong> to clear the chat history and start a new conversation thread.</li> |
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</ol> |
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<p> <strong>Powered by NVIDIA's cutting-edge AI API, Gemma 2B offers an unparalleled opportunity to interact with an AI model of exceptional conversational ability, accessible to everyone at no cost.</strong></p> |
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<p> <strong>HF Created by:</strong> @artificialguybr (<a href="https://twitter.com/artificialguybr">Twitter</a>)</p> |
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<p> <strong>Discover more:</strong> <a href="https://artificialguy.com">artificialguy.com</a></p> |
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""" |
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gr.Markdown(description) |
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chatbot = gr.Chatbot() |
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message = gr.Textbox(label="What do you want to chat about?", placeholder="Ask me anything.", lines=3) |
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submit = gr.Button(value="Send message") |
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clear = gr.Button(value="New topic") |
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system_msg = gr.Textbox(BASE_SYSTEM_MESSAGE, label="System Message", placeholder="System prompt.", lines=5) |
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max_tokens = gr.Slider(20, 1024, label="Max Tokens", step=20, value=1024, interactive=True) |
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temperature = gr.Slider(0.0, 1.0, label="Temperature", step=0.1, value=0.7, interactive=True) |
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top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95, interactive=True) |
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chat_history_state = gr.State([]) |
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def update_chatbot(message, chat_history, system_message, max_tokens, temperature, top_p): |
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print("Updating chatbot...") |
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if not chat_history or (chat_history and chat_history[-1]["role"] != "user"): |
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chat_history = user(message, chat_history, system_message if not chat_history else None) |
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else: |
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chat_history = user(message, chat_history) |
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chat_history, _ = chat(chat_history, system_message, max_tokens, temperature, top_p) |
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formatted_chat_history = [] |
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for user_msg, assistant_msg in zip([msg["content"].strip() for msg in chat_history if msg["role"] == "user"], |
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[msg["content"].strip() for msg in chat_history if msg["role"] == "assistant"]): |
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if user_msg or assistant_msg: |
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formatted_chat_history.append([user_msg, assistant_msg]) |
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return formatted_chat_history, chat_history, "" |
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submit.click( |
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fn=update_chatbot, |
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inputs=[message, chat_history_state, system_msg, max_tokens, temperature, top_p], |
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outputs=[chatbot, chat_history_state, message] |
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) |
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clear.click( |
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fn=clear_chat, |
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inputs=[chat_history_state, message], |
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outputs=[chat_history_state, message] |
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) |
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demo.launch() |
