artificialguybr
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Parent(s):
60c1103
Create app.py
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app.py
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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/008cff6d-4f4c-4514-b61e-bcfad6ba52a7"
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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: # Check if a system message is provided and should be added
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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}") # Imprime o payload enviado
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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}") # Imprime o payload recebido
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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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# Gradio interface setup
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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("Smaug 72B 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 Smaug 72B</strong>
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</div>
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<p>Smaug-72B is a large language model developed by Abacus.AI by finetuning a Qwen-72B based model, ultimately LLAMA2 architecture, using the DPO-Positive (DPOP) technique.
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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 Smaug 72B.</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, Smaug 72B 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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# Ajuste na definição da função update_chatbot para aceitar o valor atualizado do system_msg
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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: # Verify if either message is not empty
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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()
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