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 +13,19 @@ 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():
+    """Clears the chat history and message state."""
+    print("Clearing chat...")
+    chat_history_state.value = []
+    chatbot.textbox.value = ""
+
+
 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 +33,9 @@ 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 +44,69 @@ 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>
-"""
+            
+                Explore the Capabilities of LLAMA 2 70B
+            
+            Llama 2 is a large language AI model capable of generating text and code in response to prompts. 
+             How to Use:
+            
+                Enter your message in the textbox to start a conversation or ask a question.
+                Adjust the parameters in the "Additional Inputs" accordion to control the model's behavior.
+                Use the buttons below the chatbot to submit your query, clear the chat history, or perform other actions.
+            
+             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.
+             HF Created by: @artificialguybr (Twitter)
+             Discover more: artificialguy.com
+            """
             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 = ""
-
-            chatbot.clear()
+    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",
+    )
+
+    # Corrected clear button assignment:
+    chatbot.clear(outputs=[chat_history_state, chatbot.textbox])
 
 demo.launch()