Update app.py

app.py

@@ -4,32 +4,20 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 import torch
 import spaces
-"""
-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") #to change with the model
+# Initialize the client with your model
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")  # Replace with your model's name or endpoint
+
+default_system = 'You are a helpful assistant'
 
 @spaces.GPU
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+def respond(message, history, 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]})
-
+    for user, assistant in history:
+        messages.append({"role": "user", "content": user})
+        messages.append({"role": "assistant", "content": assistant})
     messages.append({"role": "user", "content": message})
 
     response = ""
-
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -38,29 +26,51 @@ def respond(
         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="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,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def clear_session():
+    return "", []
+
+def modify_system_session(system):
+    if not system:
+        system = default_system
+    return system, system, []
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align: center;'>LLM.C 1.5B Chat Demo 🤖</h1>")
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            system_input = gr.Textbox(value=default_system, lines=1, label='System Prompt')
+        with gr.Column(scale=3):
+            modify_system = gr.Button("🛠️ Set system prompt and clear history")
+    
+    system_state = gr.Textbox(value=default_system, visible=False)
+    chatbot = gr.Chatbot(label='LLM.C Chat')
+    message = gr.Textbox(lines=1, label='Your message')
+    
+    with gr.Row():
+        clear_history = gr.Button("🧹 Clear history")
+        submit = gr.Button("🚀 Send")
+    
+    with gr.Accordion("Advanced Settings", open=False):
+        max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens")
+        temperature = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
+        top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (Nucleus Sampling)")
+
+    # Set up event handlers
+    message.submit(respond, inputs=[message, chatbot, system_state, max_tokens, temperature, top_p], outputs=[chatbot])
+    submit.click(respond, inputs=[message, chatbot, system_state, max_tokens, temperature, top_p], outputs=[chatbot])
+    clear_history.click(fn=clear_session, inputs=[], outputs=[message, chatbot])
+    modify_system.click(fn=modify_system_session, inputs=[system_input], outputs=[system_state, system_input, chatbot])
 
+    gr.Markdown(
+        """
+        ## About LLM.C
+        Some stuff about llm.c + link to the github repo
+        """
+    )
 
 if __name__ == "__main__":
     demo.launch()