Update app.py

app.py

@@ -1,17 +1,25 @@
 import gradio as gr
+import openai
 import time
 import re
 import os
 
-# Available models
-MODEL = "models/mistralai/Mixtral-8x7B-Instruct-v0.1"
+MODELS = [
+    "Mixtral-8x7B-Instruct-v0.1"
+]
+
 
 # Sambanova API base URL
 API_BASE = "https://api.sambanova.ai/v1"
 
-def create_client():
-    """Creates an client instance."""
-    return 
+def create_client(api_key=None):
+    """Creates an OpenAI client instance."""
+    if api_key:
+        openai.api_key = api_key
+    else:
+        openai.api_key = os.getenv("API_KEY")
+
+    return openai.OpenAI(api_key=openai.api_key, base_url=API_BASE)
 
 def chat_with_ai(message, chat_history, system_prompt):
     """Formats the chat history for the API call."""
@@ -24,13 +32,15 @@ def chat_with_ai(message, chat_history, system_prompt):
     messages.append({"role": "user", "content": message})
     return messages
 
-def respond(message, chat_history, system_prompt, thinking_budget):
+def respond(message, chat_history, model, system_prompt, thinking_budget, api_key):
     """Sends the message to the API and gets the response."""
+    client = create_client(api_key)
     messages = chat_with_ai(message, chat_history, system_prompt.format(budget=thinking_budget))
     start_time = time.time()
 
     try:
-        response = 
+        completion = client.chat.completions.create(model=model, messages=messages)
+        response = completion.choices[0].message.content
         thinking_time = time.time() - start_time
         return response, thinking_time
     except Exception as e:
@@ -39,4 +49,96 @@ def respond(message, chat_history, system_prompt, thinking_budget):
 
 def parse_response(response):
     """Parses the response from the API."""
-    answer_match = re.search(r'<answer>(.*?)
+    answer_match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)
+    reflection_match = re.search(r'<reflection>(.*?)</reflection>', response, re.DOTALL)
+
+    answer = answer_match.group(1).strip() if answer_match else ""
+    reflection = reflection_match.group(1).strip() if reflection_match else ""
+    steps = re.findall(r'<step>(.*?)</step>', response, re.DOTALL)
+
+    if answer == "":
+        return response, "", ""
+
+    return answer, reflection, steps
+
+def generate(message, history, model, system_prompt, thinking_budget, api_key):
+    """Generates the chatbot response."""
+    response, thinking_time = respond(message, history, model, system_prompt, thinking_budget, api_key)
+
+    if response.startswith("Error:"):
+        return history + [({"role": "system", "content": response},)], ""
+
+    answer, reflection, steps = parse_response(response)
+
+    messages = []
+    messages.append({"role": "user", "content": message})
+
+    formatted_steps = [f"Step {i}: {step}" for i, step in enumerate(steps, 1)]
+    all_steps = "\n".join(formatted_steps) + f"\n\nReflection: {reflection}"
+
+    messages.append({"role": "assistant", "content": all_steps, "metadata": {"title": f"Thinking Time: {thinking_time:.2f} sec"}})
+    messages.append({"role": "assistant", "content": answer})
+
+    return history + messages, ""
+
+# Define the default system prompt
+DEFAULT_SYSTEM_PROMPT = """
+You are a helpful assistant in normal conversation.
+When given a problem to solve, you are an expert problem-solving assistant. 
+Your task is to provide a detailed, step-by-step solution to a given question. 
+Follow these instructions carefully:
+1. Read the given question carefully and reset counter between <count> and </count> to {budget}
+2. Generate a detailed, logical step-by-step solution.
+3. Enclose each step of your solution within <step> and </step> tags.
+4. You are allowed to use at most {budget} steps (starting budget), 
+   keep track of it by counting down within tags <count> </count>, 
+   STOP GENERATING MORE STEPS when hitting 0, you don't have to use all of them.
+5. Do a self-reflection when you are unsure about how to proceed, 
+   based on the self-reflection and reward, decides whether you need to return 
+   to the previous steps.
+6. After completing the solution steps, reorganize and synthesize the steps 
+   into the final answer within <answer> and </answer> tags.
+7. Provide a critical, honest and subjective self-evaluation of your reasoning 
+   process within <reflection> and </reflection> tags.
+8. Assign a quality score to your solution as a float between 0.0 (lowest 
+   quality) and 1.0 (highest quality), enclosed in <reward> and </reward> tags.
+Example format:            
+<count> [starting budget] </count>
+<step> [Content of step 1] </step>
+<count> [remaining budget] </count>
+<step> [Content of step 2] </step>
+<reflection> [Evaluation of the steps so far] </reflection>
+<reward> [Float between 0.0 and 1.0] </reward>
+<count> [remaining budget] </count>
+<step> [Content of step 3 or Content of some previous step] </step>
+<count> [remaining budget] </count>
+...
+<step>  [Content of final step] </step>
+<count> [remaining budget] </count>
+<answer> [Final Answer] </answer> (must give final answer in this format)
+<reflection> [Evaluation of the solution] </reflection>
+<reward> [Float between 0.0 and 1.0] </reward>
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Llama3.1-Instruct-O1")
+    gr.Markdown("[Powered by SambaNova Cloud, Get Your API Key Here](https://cloud.sambanova.ai/apis)")
+
+    with gr.Row():
+        api_key = gr.Textbox(label="API Key", type="password", placeholder="(Optional) Enter your API key here for more availability")
+
+    with gr.Row():
+        model = gr.Dropdown(choices=MODELS, label="Select Model", value=MODELS[0])
+        thinking_budget = gr.Slider(minimum=1, maximum=100, value=10, step=1, label="Thinking Budget", info="maximum times a model can think")
+
+    chatbot = gr.Chatbot(label="Chat", show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel", type="messages")
+
+    msg = gr.Textbox(label="Type your message here...", placeholder="Enter your message...")
+
+    gr.Button("Clear Chat").click(lambda: ([], ""), inputs=None, outputs=[chatbot, msg])
+
+    system_prompt = gr.Textbox(label="System Prompt", value=DEFAULT_SYSTEM_PROMPT, lines=15, interactive=True)
+
+    msg.submit(generate, inputs=[msg, chatbot, model, system_prompt, thinking_budget, api_key], outputs=[chatbot, msg])
+
+demo.launch(share=True, show_api=False)