Update app.py

app.py

@@ -28,11 +28,7 @@ def respond(
     response = ""
 
     for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
+        messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p
     ):
         token = message.choices[0].delta.content
 
@@ -40,95 +36,21 @@ def respond(
         yield response
 
 
-# Define the prompt template
-prompt_template = """
-Act as an expert in prompt engineering. Your task is to deeply understand what the user wants, and in return respond with a well-crafted prompt that, if fed to a separate AI, will get the exact result the user desires.
-    
-### Task:
-{task}
-    
-### Context:
-Make sure to include *any* context that is needed for the LLM to accurately, and reliably respond as needed.
-    
-### Response format:
-Outline the ideal response format for this prompt.
-    
-### Important Notes:
-- Instruct the model to list out its thoughts before giving an answer.
-- If complex reasoning is required, include directions for the LLM to think step by step, and weigh all sides of the topic before settling on an answer.
-- Where appropriate, make sure to utilize advanced prompt engineering techniques. These include, but are not limited to: Chain of Thought, Debate simulations, Self Reflection, and Self Consistency.
-- Strictly use text, no code please
-    
-### Input:
-[Type here what you want from the model]
 """
-
-# Update the `respond` function to include the prompt template
-def respond_with_prompt(
-    task,
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    # Insert the task into the prompt template
-    prompt = prompt_template.format(task=task)
-
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        prompt=prompt,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-# Define the ChatInterface with additional inputs
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
 demo = gr.ChatInterface(
-    respond_with_prompt,
+    respond,
     additional_inputs=[
+        gr.Textbox(value="**I am a large language model trained on a massive dataset of text and code.  I can follow your instructions and complete your requests thoughtfully.  I will use my knowledge to craft the perfect prompt for your desired outcome.**", label="System message"),
+        gr.Textbox(label="**Task:**", placeholder="Write your desired task here"),
+        gr.Textbox(label="**Context:**", placeholder="Provide any relevant background information"),
         gr.Textbox(
-            value="Describe your task...",
-            label="Task Description",
-            lines=7,
-        ),
-        gr.Textbox(
-            value="You are a friendly Chatbot.",
-            label="System message",
-        ),
-        gr.Slider(
-            minimum=1,
-            maximum=2048,
-            value=512,
-            step=1,
-            label="Max new tokens",
-        ),
-        gr.Slider(
-            minimum=0.1,
-            maximum=4.0,
-            value=0.7,
-            step=0.1,
-            label="Temperature",
+            label="**Response format:**",
+            placeholder="Specify how you want the output presented (e.g., list, code, essay)",
         ),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(
             minimum=0.1,
             maximum=1.0,
@@ -139,5 +61,6 @@ demo = gr.ChatInterface(
     ],
 )
 
+
 if __name__ == "__main__":
     demo.launch()