System prompt

app.py

@@ -1,23 +1,20 @@
 import gradio as gr
 import os
 import spaces
-from transformers import GemmaTokenizer, AutoModelForCausalLM
 from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
 from threading import Thread
 
 # Set an environment variable
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 
-
 DESCRIPTION = '''
 <div>
-<h1 style="text-align: center;">deepseek-ai/DeepSeek-R1-Distill-Llama-8B</h1>
+<h1 style="text-align: center;">Mistral Chat</h1>
 </div>
 '''
 
 LICENSE = """
 <p/>
-
 ---
 """
 
@@ -28,7 +25,6 @@ PLACEHOLDER = """
 </div>
 """
 
-
 css = """
 h1 {
   text-align: center;
@@ -45,7 +41,8 @@ h1 {
 
 # Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("mistralai/Ministral-8B-Instruct-2410")
-model = AutoModelForCausalLM.from_pretrained("mistralai/Ministral-8B-Instruct-2410", device_map="auto")  # to("cuda:0") 
+model = AutoModelForCausalLM.from_pretrained("mistralai/Ministral-8B-Instruct-2410", device_map="auto")
+
 terminators = [
     tokenizer.eos_token_id,
     tokenizer.convert_tokens_to_ids("<|eot_id|>")
@@ -53,23 +50,30 @@ terminators = [
 
 @spaces.GPU(duration=120)
 def chat_llama3_8b(message: str, 
-              history: list, 
-              temperature: float, 
-              max_new_tokens: int
-             ) -> str:
+                    history: list, 
+                    temperature: float, 
+                    max_new_tokens: int,
+                    system_prompt: str) -> str:
     """
-    Generate a streaming response using the llama3-8b model.
+    Generate a streaming response using the Mistral-8B model.
     Args:
         message (str): The input message.
         history (list): The conversation history used by ChatInterface.
         temperature (float): The temperature for generating the response.
         max_new_tokens (int): The maximum number of new tokens to generate.
+        system_prompt (str): The system prompt to guide the assistant's behavior.
     Returns:
         str: The generated response.
     """
     conversation = []
+
+    # Include system prompt at the beginning if provided
+    if system_prompt:
+        conversation.append({"role": "system", "content": system_prompt})
+
     for user, assistant in history:
         conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+    
     conversation.append({"role": "user", "content": message})
 
     input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
@@ -77,14 +81,14 @@ def chat_llama3_8b(message: str,
     streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
 
     generate_kwargs = dict(
-        input_ids= input_ids,
+        input_ids=input_ids,
         streamer=streamer,
         max_new_tokens=max_new_tokens,
         do_sample=True,
         temperature=temperature,
         eos_token_id=terminators,
     )
-    # This will enforce greedy generation (do_sample=False) when the temperature is passed 0, avoiding the crash.             
+
     if temperature == 0:
         generate_kwargs['do_sample'] = False
         
@@ -94,45 +98,37 @@ def chat_llama3_8b(message: str,
     outputs = []
     for text in streamer:
         outputs.append(text)
-        #print(outputs)
         yield "".join(outputs)
         
 
 # Gradio block
-chatbot=gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
+chatbot = gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')
 
 with gr.Blocks(fill_height=True, css=css) as demo:
     
     gr.Markdown(DESCRIPTION)
+
+    system_prompt_input = gr.Textbox(
+        label="System Prompt",
+        placeholder="Enter system instructions for the model...",
+        lines=2
+    )
+
     gr.ChatInterface(
         fn=chat_llama3_8b,
         chatbot=chatbot,
         fill_height=True,
         additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
         additional_inputs=[
-            gr.Slider(minimum=0,
-                      maximum=1, 
-                      step=0.1,
-                      value=0.8, 
-                      label="Temperature", 
-                      render=False),
-            gr.Slider(minimum=128, 
-                      maximum=4096,
-                      step=1,
-                      value=4096, 
-                      label="Max new tokens", 
-                      render=False ),
-            ],
+            system_prompt_input,
+            gr.Slider(minimum=0, maximum=1, step=0.1, value=0.8, label="Temperature", render=False),
+            gr.Slider(minimum=128, maximum=4096, step=1, value=4096, label="Max new tokens", render=False),
+        ],
         examples=[
-            ['How to setup a human base on Mars? Give short answer.'],
-            ['Explain theory of relativity to me like I’m 8 years old.'],
-            ['What is 9,000 * 9,000?'],
-            ['Write a pun-filled happy birthday message to my friend Alex.'],
-            ['Justify why a penguin might make a good king of the jungle.']
-            ],
-        cache_examples=False,
-                     )
-    
+            ['Are you a sentient being?']
+        ],
+        cache_examples=False
+    )
+
 if __name__ == "__main__":
     demo.launch()
-