Update app.py

app.py

@@ -1,9 +1,5 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
@@ -28,27 +24,24 @@ 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
 
         response += token
         yield response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
 demo = gr.ChatInterface(
     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(
-            label="**Response format:**",
-            placeholder="Specify how you want the output presented (e.g., list, code, essay)",
-        ),
+        gr.Textbox(value="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]", 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"),
         gr.Slider(
@@ -63,4 +56,4 @@ demo = gr.ChatInterface(
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()