Update app.py

app.py

@@ -1,38 +1,31 @@
 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;">Meta Llama3 8B</h1>
-<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/StevenChen16/llama3-8b-Lawyer"><b>Meta Llama3 8b Chat</b></a>. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!</p>
-<p>🔎 For more details about the Llama3 release and how to use the model with <code>transformers</code>, take a look <a href="https://huggingface.co/blog/llama3">at our blog post</a>.</p>
-<p>🦕 Looking for an even more powerful model? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for Meta Llama 3 70b</p>
+<h1 style="text-align: center;">AI Lawyer</h1>
+<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/StevenChen16/llama3-8b-Lawyer"><b>StevenChen16/llama3-8b-Lawyer</b></a>. This model is fine-tuned to provide legal information and assist with a wide range of legal questions. Feel free to ask anything!</p>
 </div>
 '''
 
 LICENSE = """
 <p/>
 ---
-Built with Meta Llama 3
+Built with model "StevenChen16/Llama3-8B-Lawyer", based on "meta-llama/Meta-Llama-3-8B"
 """
 
 PLACEHOLDER = """
 <div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
-   <img src="https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/8e75e61cc9bab22b7ce3dec85ab0e6db1da5d107/Meta_lockup_positive%20primary_RGB.jpg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.55;  "> 
-   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Meta llama3</h1>
-   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything...</p>
+   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">AI Lawyer</h1>
+   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Ask me anything about US and Canada law...</p>
 </div>
 """
 
-
 css = """
 h1 {
   text-align: center;
@@ -48,28 +41,13 @@ h1 {
 
 # Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("StevenChen16/llama3-8b-Lawyer")
-model = AutoModelForCausalLM.from_pretrained("StevenChen16/llama3-8b-Lawyer", device_map="auto")  # to("cuda:0") 
+model = AutoModelForCausalLM.from_pretrained("StevenChen16/llama3-8b-Lawyer", device_map="auto")
 terminators = [
     tokenizer.eos_token_id,
-    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+    tokenizer.convert_tokens_to_ids("")
 ]
 
-@spaces.GPU(duration=120)
-def chat_llama3_8b(message: str, 
-              history: list, 
-              temperature: float, 
-              max_new_tokens: int
-             ) -> str:
-    """
-    Generate a streaming response using the llama3-8b 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.
-    Returns:
-        str: The generated response.
-    """
+def chat_llama3_8b(message: str, history: list, temperature: float, max_new_tokens: int) -> str:
     conversation = []
     for user, assistant in history:
         conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
@@ -87,7 +65,6 @@ def chat_llama3_8b(message: str,
         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
         
@@ -97,15 +74,12 @@ 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:
-    
+with gr.Blocks(css=css) as demo:
     gr.Markdown(DESCRIPTION)
     gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
     gr.ChatInterface(
@@ -128,16 +102,15 @@ with gr.Blocks(fill_height=True, css=css) as demo:
                       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.']
-            ],
+            ['What are the key differences between a sole proprietorship and a partnership?'],
+            ['What legal steps should I take if I want to start a business in the US?'],
+            ['Can you explain the concept of "duty of care" in negligence law?'],
+            ['What are the legal requirements for obtaining a patent in Canada?'],
+            ['How can I protect my intellectual property when sharing my idea with potential investors?']
+        ],
         cache_examples=False,
-                     )
-    
+    )
     gr.Markdown(LICENSE)
-    
+
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)