Update app.py

app.py

@@ -1,132 +1,58 @@
 import gradio as gr
 import time
 import re
-import os
-
-MODELS = [
-    "Mixtral-8x7B-Instruct-v0.1"
-]
-
-
-# Sambanova API base URL
-API_BASE = "https://api.sambanova.ai/v1"
 
+MODELS = ["Mixtral-8x7B-Instruct-v0.1"]
 
 def chat_with_ai(message, chat_history, system_prompt):
     """Formats the chat history for the API call."""
     messages = [{"role": "system", "content": system_prompt}]
-    for tup in chat_history:
-        first_key = list(tup.keys())[0]  # First key
-        last_key = list(tup.keys())[-1]   # Last key
-        messages.append({"role": "user", "content": tup[first_key]})
-        messages.append({"role": "assistant", "content": tup[last_key]})
+    for item in chat_history:
+        messages.append({"role": "user", "content": item["user"]})
+        messages.append({"role": "assistant", "content": item.get("assistant", "")})
     messages.append({"role": "user", "content": message})
     return messages
 
-def respond(message, chat_history, model, system_prompt, thinking_budget, api_key):
-    """Sends the message to the API and gets the response."""
-
-    messages = chat_with_ai(message, chat_history, system_prompt.format(budget=thinking_budget))
-    start_time = time.time()
-
-    try:
-        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:
-        error_message = f"Error: {str(e)}"
-        return error_message, time.time() - start_time
-
-def parse_response(response):
-    """Parses the response from the API."""
-    answer_match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)
-    reflection_match = re.search(r'<reflection>(.*?)</reflection>', response, re.DOTALL)
+def respond(message, chat_history, model, system_prompt, thinking_budget):
+    """Simulate API call and get the response. Replace with actual API call."""
+    # Simulate a delay
+    time.sleep(1)
+    # Dummy response
+    response = f"Simulated response for: {message}"
+    return response, 1.0
 
-    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):
+def generate(message, history, model, system_prompt, thinking_budget):
     """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, ""
+    response, thinking_time = respond(message, history, model, system_prompt, thinking_budget)
+    history.append({"user": message, "assistant": response})
+    return history, ""
 
 # 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>
+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.
 ...
-<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)")
-
+    gr.Markdown("# Custom Chat Interface")
+    
     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")
-
+        thinking_budget = gr.Slider(minimum=1, maximum=100, value=10, step=1, label="Thinking Budget")
+    
+    system_prompt = gr.Textbox(value=DEFAULT_SYSTEM_PROMPT, lines=15, label="System Prompt")
+    chatbot = gr.Chatbot(label="Chat", type="messages")
     msg = gr.Textbox(label="Type your message here...", placeholder="Enter your message...")
+    
+    # Clear chat
+    def clear_chat():
+        return [], ""
+    
+    gr.Button("Clear Chat").click(clear_chat, inputs=None, outputs=[chatbot, msg])
 
-    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])
+    # Generate response on message submission
+    msg.submit(generate, inputs=[msg, chatbot, model, system_prompt, thinking_budget], outputs=[chatbot, msg])
 
-demo.launch(share=True, show_api=False) 
+demo.launch()