Update app.py

app.py

@@ -1,31 +1,28 @@
 import gradio as gr
 import requests
-import json
 import os
+import json
 
-# API and environment variables
-API_KEY = os.getenv('API_KEY')
+# Carrega a chave da API do ambiente ou define diretamente
+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/"
+
 headers = {
     "Authorization": f"Bearer {API_KEY}",
     "Accept": "application/json",
     "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():
-    """Clears the chat history and message state."""
-    print("Clearing chat...")
-    chat_history_state.value = []
-    chatbot.textbox.value = ""
-
 def call_nvidia_api(history, system_message, max_tokens, temperature, top_p):
-    """Calls the NVIDIA API to generate a response."""
-    messages = [{"role": "system", "content": system_message}]
-    messages.extend([{"role": "user", "content": h[0]} for h in history])
+    """Chama a API da NVIDIA para gerar uma resposta."""
+    # Prepara as mensagens, incluindo a mensagem do sistema se fornecida
+    messages = []
+    if system_message:
+        messages.append({"role": "system", "content": system_message})
+    messages.extend(history)
 
     payload = {
         "messages": messages,
@@ -34,16 +31,14 @@ def call_nvidia_api(history, system_message, max_tokens, temperature, top_p):
         "max_tokens": max_tokens,
         "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.raise_for_status()
     response_body = response.json()
-    print(f"Payload recebido: {response_body}")
     if response_body.get("choices"):
         assistant_message = response_body["choices"][0]["message"]["content"]
         return assistant_message
@@ -65,39 +60,32 @@ def chatbot_submit(message, chat_history, system_message, max_tokens_val, temper
 
     return assistant_message, chat_history
 
-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)
+# Gradio interface setup
 with gr.Blocks() as demo:
     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=chatbot_submit,
         additional_inputs=[system_msg, max_tokens, temperature, top_p],
         title="LLAMA 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>
-        """,
+        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>""",
         submit_btn="Submit",
         clear_btn="🗑️ Clear",
     )
 
-    def clear_chat():
-        chat_history_state.value = []
-        chatbot.textbox.value = ""
-
-    chatbot.clear()
-demo.launch()
+demo.launch()