Update app.py

app.py

@@ -39,15 +39,96 @@ def respond(
         response += token
         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]
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+
+# 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
 demo = gr.ChatInterface(
-    respond,
+    respond_with_prompt,
     additional_inputs=[
-        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"),
+        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",
+        ),
         gr.Slider(
             minimum=0.1,
             maximum=1.0,
@@ -58,6 +139,5 @@ demo = gr.ChatInterface(
     ],
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()