Update app.py

app.py

@@ -3,6 +3,7 @@ import requests
 import json
 import os
 
+# API and environment variables
 API_KEY = os.getenv('API_KEY')
 INVOKE_URL = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions/0e349b44-440a-44e1-93e9-abe8dcb27158"
 FETCH_URL_FORMAT = "https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/"
@@ -12,9 +13,20 @@ headers = {
  "Content-Type": "application/json",
 }
 
+# Base system message
 BASE_SYSTEM_MESSAGE = "I carefully provide accurate, factual, thoughtful, nuanced answers and am brilliant at reasoning."
 
+
+def clear_chat(chat_history_state, chat_message):
+ """Clears the chat history and message state."""
+ print("Clearing chat...")
+ chat_history_state = []
+ chat_message = ''
+ return chat_history_state, chat_message
+
+
 def user(message, history, system_message=None):
+ """Updates the chat history with the user message."""
  print(f"User message: {message}")
  history = history or []
  if system_message:
@@ -22,7 +34,9 @@ def user(message, history, system_message=None):
  history.append({"role": "user", "content": message})
  return history
 
+
 def call_nvidia_api(history, max_tokens, temperature, top_p):
+ """Calls the NVIDIA API to generate a response."""
  payload = {
  "messages": history,
  "temperature": temperature,
@@ -31,75 +45,68 @@ def call_nvidia_api(history, max_tokens, temperature, top_p):
  "stream": False
  }
  print(f"Payload enviado: {payload}")
-
  session = requests.Session()
  response = session.post(INVOKE_URL, headers=headers, json=payload)
-
  while response.status_code == 202:
  request_id = response.headers.get("NVCF-REQID")
  fetch_url = FETCH_URL_FORMAT + request_id
  response = session.get(fetch_url, headers=headers)
  response.raise_for_status()
- response_body = response.json()
- print(f"Payload recebido: {response_body}")
-
- if response_body["choices"]:
- assistant_message = response_body["choices"][0]["message"]["content"]
- history.append({"role": "assistant", "content": assistant_message})
- return history
+ response_body = response.json()
+ print(f"Payload recebido: {response_body}")
+ if response_body["choices"]:
+ assistant_message = response_body["choices"][0]["message"]["content"]
+ history.append({"role": "assistant", "content": assistant_message})
+ return history
 
-def chat(history, system_message, max_tokens, temperature, top_p, top_k, repetition_penalty):
- print("Starting chat...")
- updated_history = user(None, history, system_message)
- updated_history = call_nvidia_api(updated_history, max_tokens, temperature, top_p)
- return updated_history, ""
 
 def update_chatbot(message, chat_history, system_message, max_tokens, temperature, top_p):
+ """Updates the chatbot with the user message and generates a response."""
  print("Updating chatbot...")
- chat_history = user(message, chat_history, system_message if not chat_history else None)
+ if not chat_history or (chat_history and chat_history[-1]["role"] != "user"):
+ chat_history = user(message, chat_history, system_message)
+ else:
+ chat_history = user(message, chat_history)
  chat_history = call_nvidia_api(chat_history, max_tokens, temperature, top_p)
  return chat_history
 
+
+# Gradio interface components
+system_msg = gr.Textbox(BASE_SYSTEM_MESSAGE, label="System Message", placeholder="System prompt.", lines=5)
+max_tokens = gr.Slider(20, 1024, label="Max Tokens", step=20, value=1024)
+temperature = gr.Slider(0.0, 1.0, label="Temperature", step=0.1, value=0.2)
+top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.7)
+
 with gr.Blocks() as demo:
  with gr.Row():
  with gr.Column():
  gr.Markdown("LLAMA 2 70B Free Demo")
  description = """
-<div style="text-align: center; font-size: 1.5em; margin-bottom: 20px;">
- <strong>Explore the Capabilities of LLAMA 2 70B</strong>
-</div>
-<p>Llama 2 is a large language AI model capable of generating text and code in response to prompts.</p>
-<p><strong>How to Use:</strong></p>
-<ol>
- <li>Enter your <strong>message</strong> in the textbox to start a conversation or ask a question.</li>
- <li>Adjust the parameters in the "Additional Inputs" accordion to control the model's behavior.</li>
- <li>Use the buttons below the chatbot to submit your query, clear the chat history, or perform other actions.</li>
-</ol>
-<p><strong>Powered by NVIDIA's cutting-edge AI API, LLAMA 2 70B offers an unparalleled opportunity to interact with an AI model of exceptional conversational ability, accessible to everyone at no cost.</strong></p>
-<p><strong>HF Created by:</strong> @artificialguybr (<a href="https://twitter.com/artificialguybr">Twitter</a>)</p>
-<p><strong>Discover more:</strong> <a href="https://artificialguy.com">artificialguy.com</a></p>
-"""
+ <div style="text-align: center; font-size: 1.5em; margin-bottom: 20px;">
+ <strong>Explore the Capabilities of LLAMA 2 70B</strong>
+ </div>
+ <p>Llama 2 is a large language AI model capable of generating text and code in response to prompts. </p>
+ <p> <strong>How to Use:</strong></p>
+ <ol>
+ <li>Enter your <strong>message</strong> in the textbox to start a conversation or ask a question.</li>
+ <li>Adjust the parameters in the "Additional Inputs" accordion to control the model's behavior.</li>
+ <li>Use the buttons below the chatbot to submit your query, clear the chat history, or perform other actions.</li>
+ </ol>
+ <p> <strong>Powered by NVIDIA's cutting-edge AI API, LLAMA 2 70B offers an unparalleled opportunity to interact with an AI model of exceptional conversational ability, accessible to everyone at no cost.</strong></p>
+ <p> <strong>HF Created by:</strong> @artificialguybr (<a href="https://twitter.com/artificialguybr">Twitter</a>)</p>
+ <p> <strong>Discover more:</strong> <a href="https://artificialguy.com">artificialguy.com</a></p>
+ """
  gr.Markdown(description)
 
- chat_history_state = gr.State([])
-
- system_msg = gr.Textbox(BASE_SYSTEM_MESSAGE, label="System Message", placeholder="System prompt.", lines=5)
- max_tokens = gr.Slider(20, 1024, label="Max Tokens", step=20, value=1024)
- temperature = gr.Slider(0.0, 1.0, label="Temperature", step=0.1, value=0.2)
- top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.7)
-
- chatbot = gr.ChatInterface(
- fn=lambda message, history: update_chatbot(message, history, system_msg.value, max_tokens.value, temperature.value, top_p.value),
- additional_inputs=[system_msg, max_tokens, temperature, top_p],
- title="LLAMA 2 70B Chatbot",
- submit_btn="Submit",
- clear_btn="🗑️ Clear",
- )
-
- def clear_chat():
- chat_history_state.value = []
- chatbot.textbox.value = ""
+ chat_history_state = gr.State([])
+ chatbot = gr.ChatInterface(
+ fn=lambda message, history: update_chatbot(message, history, system_msg.value, max_tokens.value, temperature.value, top_p.value),
+ additional_inputs=[system_msg, max_tokens, temperature, top_p],
+ title="LLAMA 2 70B Chatbot",
+ submit_btn="Submit",
+ clear_btn="🗑️ Clear",
+ )
 
- chatbot.clear(fn=clear_chat, outputs=[chat_history_state, chatbot.textbox])
+ chatbot.clear(fn=clear_chat, outputs=[chat_history_state, chatbot.textbox])
 
 demo.launch()