Update app.py

app.py

@@ -1,31 +1,38 @@
 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;">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>
+<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>
 </div>
 '''
 
 LICENSE = """
 <p/>
 ---
-Built with model "StevenChen16/Llama3-8B-Lawyer", based on "meta-llama/Meta-Llama-3-8B"
+Built with Meta Llama 3
 """
 
 PLACEHOLDER = """
 <div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
-   <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>
+   <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>
 </div>
 """
 
+
 css = """
 h1 {
   text-align: center;
@@ -41,13 +48,28 @@ 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")
+model = AutoModelForCausalLM.from_pretrained("StevenChen16/llama3-8b-Lawyer", device_map="auto")  # to("cuda:0") 
 terminators = [
     tokenizer.eos_token_id,
-    tokenizer.convert_tokens_to_ids("")
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
 ]
 
-def chat_llama3_8b(message: str, history: list, temperature: float, max_new_tokens: int):
+@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.
+    """
     conversation = []
     for user, assistant in history:
         conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
@@ -58,13 +80,14 @@ def chat_llama3_8b(message: str, history: list, temperature: float, max_new_toke
     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
         
@@ -74,33 +97,36 @@ def chat_llama3_8b(message: str, history: list, temperature: float, max_new_toke
     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(css=css) as demo:
+with gr.Blocks(fill_height=True, css=css) as demo:
+    
     gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
+    # gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
     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=0.95, 
-                      label="Temperature", 
-                      render=False),
-            gr.Slider(minimum=128, 
-                      maximum=4096,
-                      step=1,
-                      value=512, 
-                      label="Max new tokens", 
-                      render=False ),
-            ],
+        # additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
+        # additional_inputs=[
+        #     gr.Slider(minimum=0,
+        #               maximum=1, 
+        #               step=0.1,
+        #               value=0.95, 
+        #               label="Temperature", 
+        #               render=False),
+        #     gr.Slider(minimum=128, 
+        #               maximum=4096,
+        #               step=1,
+        #               value=512, 
+        #               label="Max new tokens", 
+        #               render=False ),
+        #     ],
         examples=[
             ['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?'],
@@ -109,8 +135,9 @@ with gr.Blocks(css=css) as demo:
             ['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(share=True)
+    demo.launch()