Update app.py

app.py

@@ -1,18 +1,30 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-def generate_prompt(
-    prompt,
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
     max_tokens,
     temperature,
     top_p,
 ):
+    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(
-        [{"role": "user", "content": prompt}],
+        messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
@@ -21,29 +33,27 @@ def generate_prompt(
         token = message.choices[0].delta.content
 
         response += token
-    return response
+        yield response
 
-# Define the Gradio interface
-demo = gr.Interface(
-    generate_prompt,
-    inputs=[
-        gr.Textbox(label="Input your prompt", lines=7, placeholder="Type your prompt here..."),
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        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(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+        gr.Slider(
+            minimum=0.1,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
     ],
-    outputs=gr.Textbox(label="Generated Prompt", lines=10),
-    title="Expert Prompt Engineering",
-    theme="compact",  # Adjust theme as desired
-    description="Input a prompt and generate a well-crafted response.",
-    example=[
-        "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.",
-        512,
-        0.7,
-        0.95
-    ],
-    button_text="Generate Prompt"
 )
 
+
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()